
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39223300
71314
10.1038/s41598-024-71314-5
Article
Influence of parental attitudes and coping styles on mental health during online teaching in the COVID-19 pandemic
Cheng Fang 1
Chen Lixian 2
Xie Huabing 3
Wang Chenglan 4
Duan Ruonan 4
Chen Dihui 5
Li Jincheng 1
Yang Hongying yanglian7478@163.com

1
Liu Lingjiang 179294022@qq.com

1
1 https://ror.org/021nfay74 grid.452715.0 0000 0004 1782 599X Department of Pediatric Psychology, Ningbo Kangning Hospital, Ningbo, 315201 Zhejiang China
2 https://ror.org/05xceke97 grid.460059.e The Second People’s Hospital of Yuhuan, Zhejiang, China
3 https://ror.org/03ekhbz91 grid.412632.0 0000 0004 1758 2270 Department of General Medicine, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
4 Huizhen Academy, NingBo, Zhejiang China
5 Gaoqiao Central Primary School, Haishu, Ningbo, Zhejiang China
2 9 2024
2 9 2024
2024
14 2037528 2 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
During the COVID-19 pandemic, the online delivery model became the primary mode of education. With multiple pressures on society and families, mental health issues for parents have become particularly pronounced. Most of the current research has focused on the psychological state of education practitioners and children, with little attention to parents’ mental health issues. Therefore, this study explored the attitudes and coping styles of parents who experienced the process of their children being taught online over a long period and the factors influencing their mental health. This cross-sectional study was conducted between November 2021 and January 2022, using an anonymous online questionnaire to survey 1500 parents with children aged 6–13 years. The Chinese versions of the Patient Health Questionnaire Depression Scale (PHQ-9), the Parenting Stress Scale (PSS), the General Mental Health Questionnaire (GHQ-12), and the Brief Coping Style Scale (SCSQ), and a related factors questionnaire were used to survey the subjects. The normal distribution of the data was examined using the Shapiro–Wilk method. A multivariate regression analysis was conducted to identify factors significantly associated with parental mental health during the COVID-19 pandemic. Only 30.24% of parents agreed with online classes during the pandemic, and 52.28% used positive coping methods during stressful situations. Multivariate regression models identified significant factors associated with parental mental health: parent’s gender, child’s grade level, perceived stress about online classes, whether the child has ADHD, positive or negative coping styles, and subjective attitudes of support for online classes or not. The results of the study suggest that as online classes become more socially acceptable, it is necessary to be concerned about the risk of mental illness for parents and develop policies and interventions, especially for parents who adopt negative coping styles and endorse online classes. The focus should be on the stress of online classes on parents, improving the acceptance of online classes and psychological well-being, regulating the way parents deal with their children, and targeting subgroups of children with ADHD symptoms during the COVID-19 pandemic.

Keywords

Online delivery
Parenting stress
Coronavirus,
Coping styles
Parental mental health
Subject terms

Public health
Lifestyle modification
Medical Science and Technology Plan Project in Ningbo2020Y22 Cheng Fang Ningbo Natural Science Foundation202003N4262 Cheng Fang Medical and Health Technology of Zhejiang Province2021KY330 Cheng Fang issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Neoconiosis is a serious respiratory disease caused by infection with the novel coronavirus SARS-COV-2, known as COVID-19. Since 2019, COVID-19 has created a global pandemic, posing a significant threat to global public health1. Although the case fatality rate of COVID-19 has been estimated at 2–3%, which is considerably lower than that of Severe Acute Respiratory Syndromes (SARS) (approximately 10%) and Middle East Respiratory Syndrome (MERS) (approximately 40%)2–4, its impact has been profound. The virus has an extremely high transmission rate through the respiratory tract and close contact. As of 15 March 2020, COVID-19 has spread rapidly to 34 provinces and cities in China, while 144 countries/regions in five continents worldwide have reported cases of infection to varying degrees5. Thus, the COVID-19 pandemic in recent years has posed a significant challenge to the entire society and every individual’s life.

With the global spread of COVID-19, education in China and worldwide is facing a considerable challenge. Anticipating the high transmission rate of the virus, the crowded nature of public places such as campuses, and the low immunity profile of adolescents, the Chinese government enacted a series of responses, of which the use of online learning became a typical result of the COVID-19 pandemic6,7. While such initiatives have played a large part in preventing and controlling the pandemic, the impact on the parent and child side of the online delivery context is currently varied. Means et al. argue that online learning is an educational tool based on technological devices and the Internet in an era of rapid development8. Tallent-Runnels et al. are similarly optimistic that technological innovations and the continued growth of Internet accessibility can increase the motivation to learn online9. By contrast, however, Joshi et al. found that the pedagogical outcomes of online learning are controversial and that online learning directly contributes to the lack of face-to-face interaction between students and teachers10. Research has shown that effective online education requires high-quality means for designing and assessing online courses and that the operators, the teachers who deliver the courses online, are equally required in terms of proficiency in the use of Internet equipment, etc11,12. Unfortunately, many educational institutions lack a careful design and development process for the transition of teaching models13, and the online education experience during the pandemic has suffered from varying degrees of skepticism and rejection14.

During the COVID-19 pandemic, the conflict between the educational format and the students translated in different ways and to varying degrees into multifaceted stress and family conflicts for parents. On the one hand, online instruction placed greater demands on the technology of connecting to the Internet, as a great challenge for many economically disadvantaged households, especially with the economic depression induced by the pandemic. Students from different family backgrounds faced different feedback on their online learning, even with the same willingness to learn. In particular, younger students15,16, students from immigrant backgrounds17, and students from families with a low socioeconomic status18 encountered more difficulties with distance learning. At the same time, the prolonged online mode of delivery poses a risk of increased exposure of learners to screens, impacting young people’s visual health. The lack of teacher and parental supervision is another challenge, especially for young learners. The disadvantaged, weaker learners, requiring more supervision and guidance from their elders, face significant difficulties in this learning environment. Beyond China, the pandemic and the closure of schools have profoundly affected the mental health of students worldwide19. Empirical studies in Bangladesh, China, France, Greece, the UK, and the USA have found that a large proportion of students suffer from varying degrees of mental disorders20–22. Many students experiencing the online delivery model over a long period report suffering from depression, anxiety, distress, and even suicidal thoughts23,24. Interestingly, this academic mental health problem of adolescents is, to some extent, transferred to their parents. Due to multiple issues, such as the inadaptability of the online mode of delivery and socioeconomic pressures, the academic burden of the children leads to different aspects of psychological feedback and coping for the parents.

Apart from the financial pressures on some families, parents and children in the context of their different formative years show very different attitudes and behavioral patterns toward the use and efficacy of electronic devices. This phenomenon has been particularly notable in online education during the COVID-19 pandemic. According to Soykan, parents emphasize the expected dangers of using technology for academic performance25. The epidemic forced parents to take on an additional task: supervising children during class, especially at basic and primary levels. Research on emergency distance learning during the COVID-19 pandemic has shown reduced interactions between teachers and students26, with the ability of students to learn independently and actively being crucial to students’ academic performance27. Hale, Troxel, and Buysse surveyed online teaching and concluded that parents were disappointed in helping their children focus and participate in virtual classrooms28. The study reported emotional and behavioral problems (i.e., anxiety, irritability, and distraction) observed by parents in their children and adolescents due to the epidemic29. These phenomena are particularly significant in adolescents with attention deficit hyperactivity disorder (ADHD). Some relevant studies have shown that adolescents with ADHD find it more difficult to adapt to the online teaching mode compared to their peers and often exhibit problems such as motivation problems and executive dysfunction30. ADHD children face significant challenges in online learning environments, including attention, task organization, and completion difficulties. The lack of face-to-face supervision and structure can make it harder for them to adapt and participate in learning activities. Additionally, the demands for self-discipline and time management in online learning pose further obstacles for these children31. Parents often bear different levels of pressure and mental health problems due to the problems associated with electronic equipment, anxiety about performance under online learning, and comprehensive social pressure during the COVID-19 pandemic. Research has shown that parents have greater responsibility and participation in their children’s learning in a distance learning environment than in regular classroom teaching32. This is a vicious circle of contradictions, wherein the various forms of pressure on parents may affect how students experience a new learning environment: the greater the pressure their parents feel, the more negative their views of distance learning become.

Under enormous social and economic pressure, the threat of COVID-19 infection, and the anxiety about children’s academic performance, parents experience mental health problems to varying degrees, which impact their social development. Studies have shown that home isolation, economic hardship, despair, and loneliness during the COVID-19 pandemic also increased the risk of mental illness and suicide33,34. Social isolation is usually closely related to physical and mental health issues35. Such mental health problems often have an inseparable relationship with the parents’ attitudes toward online teaching during the COVID-19 pandemic and the measures taken, which are often unavoidable. However, no scholars have yet reported on this phenomenon.

Therefore, this study investigated the parents’ sociodemographic information and mental health scoring variables (depression level, parenting stress, general psychological state, and coping style) to analyze differences in parental mental health and their related factors under different coping styles and attitudes toward online courses and explore how factors such as children’s ADHD symptoms, parents’ attitudes toward online courses, and parents’ coping styles affect parents’ mental health, in an attempt to provide effective guidance on parental mental health issues in the post-pandemic era and the emerging wave of online education.

Methods

Study design

This cross-sectional study surveyed the parents of young children. In the context of the COVID-19 pandemic and the prevalence of online classes, parents’ mental health variables, including depression levels, parental stress, general psychological status, and coping styles, were measured using an anonymous online survey, and their children’s ADHD symptoms were measured through parent ratings. Ethical approval for this study was obtained from the Ethics Committee of Kangning Hospital, Ningbo, Zhejiang Province, China (No. NBKNYY-2021-LC-1). A random sample of six primary schools in Ningbo, Zhejiang Province and Yuhuan County, Zhejiang Province were selected for data collection from November 2021 to January 2022. The purpose and content of the survey were explained to each respondent, and a digital informed consent form was provided for the study. All the respondents completed the questionnaire anonymously.

During this period, China adopted strict measures to control the spread of the COVID-1936. Especially in Zhejiang Province, including Ningbo City and Yuhuan County, the government and education departments quickly adapted to the changing situation, ensuring the continuity of education while prioritizing public health. Therefore, many schools in China still used online learning modes due to the ongoing impact of the COVID-19 pandemic. In primary schools in Ningbo City and Yuhuan County, Zhejiang Province, these regions benefited from relatively developed digital infrastructure. Schools were equipped with the necessary technology, allowing students to access the internet and use digital devices, although there were still differences. Platforms such as DingTalk, WeChat, and specialized educational applications were widely used for teaching and maintaining communication between teachers, students, and parents37,38. Concurrently, teachers received training on effectively using online teaching tools and methods. Additionally, a support system was established to assist parents and students in coping with the new educational model.

Participants

The questionnaires were completed anonymously in online class groups, and the participants were selected from different regions. The inclusion criteria were: (i) parents of children studying in Ningbo or Yuhuan County, Zhejiang Province, China, before the COVID-19 outbreak and currently, including primary school students and junior high school students; (ii) a child age of 6–13 years. Only parents participated in this survey; children did not directly participate.

Our study’s sample size was determined based on established principles in international questionnaire design. These guidelines suggest that the sample size should be approximately 5 to 20 times the number of items in the questionnaire to ensure adequate data representation and reliability39,40. Anticipating the possibility of non-responses or invalid questionnaires, which typically account for about 10% of the total surveys distributed41, we increased our target sample size by 10%. Therefore, the final sample size was set at 5.5 times the number of questionnaire items, ensuring that even with the anticipated rate of non-response or invalidity, we would still have a sufficiently large sample to maintain the robustness of our study’s findings. Given that our study comprised 153 items, the minimum sample size was calculated at 842 (153 items × 5.5).

In the end, 1500 questionnaires were distributed, and 1454 questionnaires were returned, yielding a response rate of 96.93%. Among these questionnaires, 138 had more than 30% missing data and logical errors and were thus recorded as invalid. There were 1,316 valid questionnaires, resulting in an effective completion rate of 90.51%.

Survey content

The general questionnaire comprised two main sections. The first part of the survey concerned sociodemographic information such as child grade, parent gender, parent education, online class approval or not, online class pressure, children’s ADHD type, and parents’ perceived source of their emotional impact (i.e., whether they believed their emotional impact was due to the pandemic or their child’s online classes). The specific question was: “If you feel that your emotions are affected, do you think it is due to the pandemic, your child’s online learning, or both?” Children’s ADHD type was rated by the parent version of the SNAP-IV rating scale42. The categories were as follows: attention deficit dominant (≥ 6 items on the Attention Deficit Subscale 2 or 3 only), hyperactivity/impulsivity dominant (≥ 6 items on the Hyperactivity/Impulsivity Subscale 2 or 3 only), and ADHD-C (≥ 6 items on both the Attention Deficit and Hyperactivity/Impulsivity Subscale 2 or 3).

The second part concerned the mental health rating variables, including depression level, parental stress, general psychological state, and coping styles, measured by the Patient Health Questionnaire Depression Scale (PHQ-9) (0–27), the Parental Stress Scale (PSS), the General Mental Health Questionnaire (GHQ-12) (0–12), and the Brief Coping Style Scale (SCSQ). These scales have exhibited good reliability and validity in previous studies43–46. These scales are classified as follows: PHQ-9: normal (0–4), mild (5–9), moderate to severe (10–14), and severe (20–27); PSS: normal (< 86), critical high (86–90), high (91–98), and very high (> 98); GHQ-12: high risk (98); GHQ-12 high (86o–3), and low risk (0–1); SCSQ: positive coping (coping tendency > 0), negative coping (coping tendency < 0), where coping tendency = positive coping standard score (Z score)—negative coping standard score (Z score), and the standard score is Z-transformed using the mean and standard deviation of positive coping style and negative coping style.

To ensure the reliability and consistency of the questionnaire results, psychiatrists explained the purpose of the survey and administered it to participants. A pre-test was conducted before the main survey to explore the questionnaire’s psychometric properties, such as reliability and validity. The pre-test involved a small group of participants similar to the main study’s target population. After completing the pre-test questionnaire, the researchers collected the responses, entered the data, and performed an initial analysis to identify any issues with the questionnaire items. This process helped refine the questionnaire and ensure the quality of the answers in the main survey.

Data analysis methods

After logic checking and proofreading, we used R-4.2.1 (an open-source programming language) and Rstudio for Windows (an open-source IDE) to process and analyze the data. We first conducted a descriptive analysis of the social demographic characteristics of the participants (Table 1) and a descriptive analysis of the continuous variables (PSS, PHQ-9, GHQ-12, and SCSQ) (Table 2).Table 1 The sociodemographic characteristics of participants.

Variables (categorical)	Overall	%	
Total	1316	100	
Child grade	
 1–3	645	49	
 4–6	264	20.1	
 7–9	407	30.9	
Parent gender	
 Female	855	65.0	
 Male	461	35.0	
Parent age	
  < 30	21	1.6	
  > 45	142	10.8	
 30–37	655	49.8	
 38–45	498	37.8	
Parent education	
 Primary school	78	5.9	
 Middle school	391	29.7	
 High school	333	25.3	
 College	497	37.8	
 Graduate school	17	1.3	
Online class approval	
 Approve	398	30.2	
 Disapprove	918	69.8	
Online class pressure	
 Very high	224	17.0	
 High	449	34.1	
 Moderate	558	42.4	
 No pressure	85	6.5	
Emotional source	
 Pandemic impact	251	19.1	
 Online learning	235	17.9	
 Both	830	63.1	
Children's ADHD type	
 Normal	1242	94.4	
 Combined type	19	1.4	
 Hyperactive impulsiv type	15	1.1	
 Inattentive type	40	3.0	

Table 2 The distribution characteristics of scale score variables.

Variables (scale score)	Mean	SD	Skewness	Kurtosis	Normality (Shapiro–Wilk)	
PSS	104.261	20.921	0.038	3.701	 < 0.001***	
PHQ-9	10.751	3.331	2.801	13.680	 < 0.001***	
GHQ-12	2.886	1.956	2.123	8.8808	 < 0.001***	
SCSQ	0.003	1.025	0.219	3.032	 < 0.001***	
***p < 0.001.

The Shapiro–Wilk test47 was used to test the normality of each variable. In addition, Spearman’s rank correlation analysis48 was used to investigate the correlations between the variables (Table 3). Spearman’s rank correlation analysis was adopted because it is applicable to data with non-normal distribution and can effectively evaluate the monotonic relationship between two variables. Its results are more reliable than the chi-squared test, especially when the data are skewed. Ultimately, this study conducted a univariate intergroup analysis on each variable based on whether the parents agreed to online classes and the parents’ coping styles (Table 4). The purpose was to explore the differences in parental mental health and related factors among different groups and determine possible influencing factors.Table 3 Spearman rank correlation among variables.

	CG	PG	PA	PE	OLA	OLP	ES	PSS	PHQ	GHQ	ADHD	SCSQ	
CG													
PG	0.01												
PA	0.5***	 − 0.12***											
PE	 − 0.45***	 − 0.01	 − 0.32***										
OLA	 − 0.02	 − 0.01	 − 0.01	 − 0.01									
OLP	 − 0.04	0.07*	0.04	0.15***	 − 0.33***								
ES	 − 0.08**	0.09***	 − 0.05	0	0.05	 − 0.13***							
PSS	0.02	 − 0.08**	0.04	 − 0.17***	0.15***	 − 0.37***	0.1***						
PHQ	0.12***	 − 0.04	0.08**	 − 0.07**	0.13***	 − 0.21***	0.17***	0.38***					
GHQ	0.14***	 − 0.05	0.09***	 − 0.14***	0.16***	 − 0.27***	0.15***	0.36***	0.54***				
ADHD	 − 0.08**	 − 0.1***	 − 0.01	0.03	0.11***	 − 0.16***	0.07*	0.18***	0.15***	0.11***			
SCSQ	 − 0.01	0.05	 − 0.03	0.05	 − 0.06*	0.07**	0	 − 0.33***	 − 0.21***	 − 0.19***	 − 0.07*		
CG—Child Grade; PG—Parent Gender; PA—Parent Age; PE—Parent Education; OLA—Online Class Approval; OLP—Online Class Pressure; ES—Emotional Source; PSS—Perceived Stress Scale; PHQ—Patient Health Questionnaire; GHQ—General Health Questionnaire; ADHD—Attention Deficit Hyperactivity Disorder.

SCSQ—Simplified Coping Style Questionnaire.

*p < 0.05, **p < 0.01, ***p < 0.001.

Table 4 Single-factor analysis of participant survey data.

	OLC Disapprove	OLC Approve	p-Value	t\χ2	Negative coping	Positive coping	p-Value	t\χ2	
Total	918	398			688	628			
Child grade (%)	
 1–3	445 (48.5)	200 ( 50.3)	0.829	0.374	337 (49.0)	308 (49.0)	1	0.001	
 4–6	187 (20.4)	77 ( 19.3)	138 (20.1)	126 (20.1)	
 7–9	286 (31.2)	121 ( 30.4)	213 (31.0)	194 (30.9)	
Parent gender = Female (%)	598 (65.1)	257 ( 64.6)	0.892	0.018	432 (62.8)	423 (67.4)	0.094	2.810	
Parent age (%)	
  < 30	16 ( 1.7)	5 ( 1.3)	0.799	1.007	9 ( 1.3)	12 ( 1.9)	0.085	6.619	
  > 45	101 (11.0)	41 ( 10.3)	88 (12.8)	54 ( 8.6)	
 30–37	450 (49.0)	205 ( 51.5)	334 (48.5)	321 (51.1)	
 38–45	351 (38.2)	147 ( 36.9)	257 (37.4)	241 (38.4)	
Parent education (%)	
 Primary School	45 ( 4.9)	33 ( 8.3)	0.105	7.666	44 ( 6.4)	34 ( 5.4)	0.14	6.928	
 Middle School	285 (31.0)	106 ( 26.6)	213 (31.0)	178 (28.3)	
 High School	230 (25.1)	103 ( 25.9)	185 (26.9)	148 (23.6)	
 College	345 (37.6)	152 ( 38.2)	237 (34.4)	260 (41.4)	
 Graduate School	13 ( 1.4)	4 ( 1.0)	9 ( 1.3)	8 ( 1.3)	
Online class pressure (%)	
 Very High	100 (10.9)	124 ( 31.2)	 < 0.001***	144.56	121 (17.6)	103 (16.4)	0.345	3.316	
 High	279 (30.4)	170 ( 42.7)	244 (35.5)	205 (32.6)	
 Moderate	459 (50.0)	99 ( 24.9)	285 (41.4)	273 (43.5)	
 No Pressure	80 ( 8.7)	5 ( 1.3)	38 ( 5.5)	47 ( 7.5)	
Emotional source (%)	
 Pandemic Impact	198 (21.6)	53 ( 13.3)	0.001***	14.77	140 (20.3)	111 (17.7)	0.235	2.893	
 Online Learning	149 (16.2)	86 ( 21.6)	113 (16.4)	122 (19.4)	
 Both	571 (62.2)	259 ( 65.1)	435 (63.2)	395 (62.9)	
ADHD type (%)	
 Combined Type	8 (0.9)	11 (2.8)	0.001***	17.255	17 (2.5)	2 (0.3)	0.007**	12.061	
 Hyperactive Impulsive Type	7 (0.8)	8 (2.0)	10 (1.5)	5 (0.8)	
 Normal	882 (96.1)	360 (90.5)	640 (93.0)	602 (95.9)	
 Inattentive Type	21 (2.3)	19 (4.8)	21 (3.1)	19 (3.0)	
PSS (mean (SD))	102.27 (20.73)	108.85 (20.65)	 < 0.001***	 − 5.298	109.37 (20.65)	98.66 (19.77)	 < 0.001 ***	9.589	
PHQ-9 (mean (SD))	10.45 (2.94)	11.45 (4.02)	 < 0.001***	 − 5.077	11.33 (3.94)	10.11 (2.35)	 < 0.001 ***	6.763	
GHQ-12 (mean (SD))	2.66 (1.69)	3.41 (2.38)	 < 0.001***	 − 6.558	3.17 (2.16)	2.57 (1.64)	 < 0.001 ***	5.607	
*p < 0.05, **p < 0.01, ***p < 0.001.

In the multiple regression analysis, we examined the impact of various independent variables on the psychological health of parents (i.e., PSS, PHQ, and GHQ) under different coping styles and attitudes toward online courses. Specifically, we studied the effects of different coping styles (negative or positive) on parental mental health and related factors (Table 5). Additionally, we explored how different attitudes toward online courses (disapproving vs. approving) influenced parental mental health and related factors (Table 6). The purpose of the multiple regression analysis was to reveal the impact of each variable on the parents’ mental health and related factors and provide a basis for interventive measures.Table 5 Comparison of Multivariate Regression Results of Parental Mental Health and Its Related Factors Under Different Coping Styles.

Variables	PSS	PHQ-9	GHQ-12	
Negactive coping	Postive coping	Negactive coping	Postive coping	Negactive coping	Postive coping	
Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	
(Intercept)	147.91	0.000***	147.31	0.000***	27.01	0.000***	34.86	0.000***	18.20	0.000***	25.07	0.000***	
Child grade	
 1–3	
 4–6	0.91	0.693	− 2.80	0.200	0.43	0.659	− 0.17	0.775	− 0.01	0.995	− 0.28	0.596	
 7–9	0.15	0.945	− 1.55	0.487	3.79	0.000***	2.56	0.000***	2.83	0.000***	2.38	0.000***	
Parent gender	
 Male	
 Female	− 4.29	0.013*	− 0.35	0.833	− 1.35	0.065	0.22	0.639	− 0.92	0.114	0.08	0.837	
Parent age	
 < 30	
 >45	− 12.16	0.110	5.40	0.390	− 2.45	0.447	− 0.99	0.573	− 2.11	0.410	− 1.18	0.430	
 30–37	− 6.99	0.331	1.56	0.783	− 3.16	0.301	0.28	0.862	− 2.92	0.229	− 0.06	0.964	
 38–45	− 7.21	0.324	4.86	0.400	− 1.86	0.548	0.94	0.558	− 1.76	0.476	0.78	0.570	
Parent education	
 Primary School	
 Middle School	0.33	0.927	− 2.75	0.452	3.36	0.026*	0.39	0.704	2.93	0.015*	− 0.36	0.676	
 High School	1.18	0.746	− 10.84	0.004**	4.39	0.005**	− 0.32	0.760	3.62	0.003**	− 0.93	0.293	
 College	− 6.81	0.068	− 10.54	0.005**	3.48	0.028*	− 0.24	0.819	2.79	0.027*	− 0.73	0.407	
 Graduate School	3.71	0.636	− 5.90	0.439	4.56	0.172	− 1.52	0.475	3.67	0.166	− 2.52	0.164	
Online class approval	
 Disapprove	
 Approve	2.04	0.268	2.27	0.215	1.62	0.039*	1.71	0.001***	1.45	0.020*	1.64	0.000***	
Online class pressure	
 Very High	
 High	− 7.59	0.002**	− 8.37	0.000***	− 1.59	0.123	− 1.47	0.027*	− 1.41	0.085	− 1.21	0.032*	
 Moderate	− 16.85	0.000***	− 15.75	0.000***	− 3.97	0.000**	− 2.08	0.002**	− 3.65	0.000***	− 2.10	0.000***	
 No Pressure	− 31.38	0.000***	− 27.46	0.000***	− 5.72	0.001***	− 2.91	0.005**	− 5.10	0.000***	− 3.33	0.000***	
Emotional source	
 Pandemic Impact	
 Online Learning	− 0.23	0.931	− 1.57	0.551	− 0.56	0.628	− 1.05	0.151	− 0.72	0.436	− 1.12	0.074	
 Emotional source Both	3.13	0.143	1.43	0.509	1.94	0.033*	1.04	0.087	1.42	0.049*	0.63	0.219	
ADHD type	
 Combined Type	
 Hyperactive Impulsive Type	− 7.57	0.375	− 9.72	0.548	1.56	0.667	− 6.66	0.141	2.16	0.454	− 4.64	0.227	
 Normal	− 3.77	0.476	− 20.19	0.140	− 1.92	0.394	− 12.82	0.001***	− 0.47	0.794	− 9.29	0.004**	
 Inattentive Type	12.72	0.069	− 7.15	0.619	3.66	0.216	− 10.07	0.012*	3.20	0.175	− 7.14	0.037*	
 Multiple R2\F-statistic \p-value	0.1772\7.57\***	0.2032\8.162\***	0.1237\4.965\***	0.1460\5.473\***	0.1346\5.468\***	0.1921\7.610\***	
The variables in the last row, 'Multiple R2', 'F-statistic', and 'p-value', indicate the key metrics of the regression model, providing information on the proportion of variance explained, the overall significance of the model, and the statistical significance of the results, respectively.

*p < 0.05, **p < 0.01, ***p < 0.001.

Table 6 Comparison of Multivariate Regression Results of Parental Mental Health and Its Related Factors Under Different Attitudes Toward Online Classes.

Variables	PSS	PHQ-9	GHQ-12	
Disapprove OLC	Approve OLC	Disapprove OLC	Approve OLC	Disapprove OLC	Approve OLC	
Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	Estimate	p-value	
(Intercept)	133.98	0.000***	129.65	0.000***	20.26	0.000***	32.39	0.000***	12.63	0.000***	23.91	0.000***	
Child grade	
 1–3	
 4–6	− 1.89	0.314	0.76	0.792	0.45	0.471	− 0.57	0.651	0.27	0.594	− 1.13	0.273	
 7–9	− 1.22	0.509	− 0.07	0.981	2.89	0.000***	3.68	0.003**	2.35	0.000***	2.84	0.005**	
Parent gender	
 Male	
 Female	− 2.03	0.147	− 3.01	0.180	− 0.12	0.793	− 1.31	0.180	− 0.12	0.747	− 0.89	0.265	
Parent age	
 < 30	
 > 45	− 7.80	0.153	15.11	0.128	− 0.22	0.903	− 3.01	0.487	0.09	0.951	− 4.16	0.238	
 30–37	− 7.43	0.142	18.01	0.051	− 0.63	0.706	− 2.69	0.503	− 0.46	0.735	− 3.62	0.268	
 38–45	− 6.56	0.204	18.89	0.043*	− 0.22	0.899	− 0.62	0.880	0.05	0.974	− 1.68	0.612	
Parent education	
 Primary School	
 Middle School	1.46	0.646	− 4.78	0.255	1.15	0.276	3.90	0.034	0.88	0.299	2.68	0.073	
 High School	− 2.61	0.421	− 6.04	0.162	2.25	0.037*	1.73	0.360	1.71	0.048*	0.80	0.601	
 College	− 6.37	0.052	− 10.35	0.020*	2.07	0.058	1.05	0.586	1.52	0.083	0.24	0.877	
 Graduate School	2.36	0.707	− 10.51	0.334	4.13	0.049*	− 5.70	0.230	2.94	0.081	− 5.75	0.138	
Online class pressure	
 Very High	
 High	− 3.96	0.090	− 9.47	0.000***	− 0.22	0.773	− 2.18	0.040*	− 0.26	0.677	− 1.88	0.030*	
 Moderate	− 11.96	0.000***	− 21.63	0.000***	− 2.23	0.003**	− 3.57	0.004**	− 2.19	0.000***	− 3.53	0.000***	
 No Pressure	− 25.00	0.000***	− 25.39	0.006**	− 3.26	0.002**	− 7.43	0.066	− 3.46	0.000***	− 6.87	0.037*	
Emotional source	
 Pandemic Impact	
 Online Learning	− 1.89	0.387	1.28	0.727	− 0.62	0.395	− 0.10	0.952	− 0.76	0.191	− 0.13	0.921	
 Emotional source Both	2.59	0.127	2.52	0.422	1.42	0.013*	1.68	0.221	0.83	0.068	1.59	0.154	
ADHD type	
 Combined Type	
 Hyperactive Impulsive Type	2.77	0.788	− 6.87	0.474	10.97	0.001***	− 5.38	0.200	9.51	0.001***	− 3.06	0.371	
 Normal	1.04	0.883	− 8.86	0.167	1.66	0.483	− 5.93	0.034*	2.35	0.216	− 3.42	0.133	
 Inattentive Type	17.58	0.033*	3.23	0.682	5.50	0.045*	− 1.69	0.623	5.18	0.019	− 0.77	0.782	
 SCSQ	− 6.54	0.000***	− 7.84	0.000***	− 1.59	0.000***	− 2.60	0.000***	− 1.29	0.000***	− 2.08	0.000***	
 Multiple R2\F-statistic \p-value	0.2730\7.469\***		0.2367\14.660\***		0.2168\5.506\***		0.1448\8.001\***		0.2019\5.033\***		0.1629\9.196\***		
The variables in the last row, 'Multiple R2', 'F-statistic', and 'p-value', indicate the key metrics of the regression model, providing information on the proportion of variance explained, the overall significance of the model, and the statistical significance of the results, respectively.

*p < 0.05, **p < 0.01, ***p < 0.001.

The main objectives of this study were to analyze the differences in parental mental health and its associated factors across coping styles and attitudes toward online classes, as well as to explore how factors such as children with ADHD symptoms, parental attitudes toward online classes, and parental coping styles affect parental mental health. We hope to provide strong references and recommendations for improving parental mental health through these analyses.

It is particularly noteworthy that this work considered p-values of < 0.05 statistically significant.

Results

Sociodemographic characteristics

The survey, which included 1316 participants, revealed that only a minority of parents supported online classes during the pandemic. The majority adopted positive coping methods during stressful situations. Table 1 presents detailed demographic information about the parents and their children, including grade levels, gender distribution, age ranges, and educational attainment. Additionally, Table 1 outlines the parents’ perceptions of online classes, their levels of stress, sources of emotional stress, and the types of ADHD diagnosed in their children.

The distribution and correlations among variables

Table 2 presents the distribution characteristics of scores on the PSS, PHQ-9, GHQ-12, and SCSQ scales, indicating that the scores did not conform to a normal distribution regarding skewness, kurtosis, and extreme value distribution ratios.

Table 3 details the correlations between sociodemographic characteristics and mental health variables, highlighting several significant associations. Key findings include: (1) Parental stress correlated significantly with the child’s grade and parent’s gender, educational level, approval of online classes, stress due to online classes, and sources of emotional stress; (2) PHQ-9 scores showed significant associations with the child’s grade and parental age, education, approval of online classes, online class stress, and sources of emotional stress; (3) GHQ-12 scores were significantly linked to the child’s grade and parental age, education, online class recognition, online class stress, and emotional sources; (4) SCSQ scores correlated with parental gender, approval of online classes, and stress from online classes; (5) ADHD was significantly associated with the child’s achievements and parental gender, recognition of online classes, stress from online classes, and emotional sources.

These correlations provided the basis for the subsequent analysis and are comprehensively detailed in Table 3.

The severity of measurements and associated factors

Table 4 shows the results of single-factor analyses of survey data, highlighting significant differences based on parental approval of online classes and the type of coping strategy adopted. The key findings are: (1) Parental approval of online classes: Significant differences were observed between parents who disapproved and those who approved of online classes in terms of online class pressure, emotional sources, and scores on the PSS, PHQ-9, and GHQ-12 scales (p < 0.001 for all); (2) Coping strategies: Parents adopting positive coping strategies (SCSQ > 0) showed significant differences in PSS, PHQ-9, and GHQ-12 scores compared to those using negative coping strategies (SCSQ < 0), with p < 0.001 for all comparisons. No significant differences were found in terms of sociodemographic characteristics or other factors. These analyses underscore the impact of online class approval and coping strategies on parental stress and mental health, as detailed in Table 4.

The study used multiple regression to analyze parental coping styles, attitudes toward online courses, and their impact on mental health, including the PSS, PHQ-9, and GHQ-12 dimensions, as detailed in Tables 5 and 6. The findings are summarized below.

The results from Table 5 are as follows: For negative coping, PSS scores were significantly associated with parental gender, online classroom stress, and ADHD type. Female parents and parents experiencing lower stress levels in online classrooms had lower PSS scores. Parents of children without ADHD symptoms also had lower PSS scores compared to those with the combined ADHD type; PHQ-9 scores were significantly associated with the child’s grade and parental education, online classroom agreement, and emotional source. Higher PHQ-9 scores were noted among parents of older children, parents with higher education, those agreeing to online classes, and those influenced by both COVID-19 and online learning. GHQ-12 scores were significantly associated with the child’s grade and parental education, online classroom agreement, and emotional source. Higher GHQ-12 scores were found among parents of older children, parents with higher education, those agreeing to online classes, and those influenced by both COVID-19 and online learning. For positive coping, PSS scores were associated with parental education and online classroom stress. Parents with higher education and those with lower online classroom stress had lower PSS scores. PHQ-9 scores correlated with the child’s grade, online classroom stress, and ADHD type. Parents of older children, those with lower online classroom stress, and those with children without ADHD symptoms had lower PHQ-9 scores. GHQ-12 scores correlated with online classroom stress and ADHD type. Parents with lower online classroom stress and those with children without ADHD symptoms had lower GHQ-12 scores.

Table 6 further compares the results of multiple regressions under different attitudes toward coping with online classes.

Concerning PSS scores, among parents disapproving of online classes, those aged 38–45 had higher scores, while college-educated parents had lower scores. Among approving parents, moderate online classroom stress and higher SCSQ scores were associated with lower PSS scores. Concerning PHQ-9 scores, among disapproving parents, those with a postgraduate degree had higher scores. Among approving parents, higher scores were noted for parents of older children and those with high online classroom stress, while higher SCSQ scores were linked to lower PHQ-9 scores. Regarding GHQ-12 scores, among disapproving parents, those with children having attention deficit ADHD had higher scores. Among approving parents, higher scores were observed for parents of older children and those with high online classroom stress, while higher SCSQ scores correlated with lower GHQ-12 scores.

These analyses illustrate the complex relationships between parental coping styles, attitudes toward online classes, and various mental health dimensions. Detailed results are presented in Tables 5 and 6.

Discussion

Since the gradual outbreak of COVID-19 in late 2019, the virus has exhibited an unimaginably high level of infectiousness49. As of 3 April 2020, there had been at least 52,869 deaths and 10,066 confirmed cases of COVID-19 infection; by 18 May 2020, the number of confirmed cases had increased to 4,679,511, with 315,005 deaths. Frighteningly, these numbers rapidly increased, with many people having secondary infections50. The rapid spread of COVID-19 worldwide posed a serious challenge to the entire human population on many fronts: health, economic, environmental, and social. The impact on the education sector has been particularly significant: to avoid the mass gathering of young students, most schools were forced to opt out of the face-to-face mode of delivery and instead turn to online learning. Although online learning is not new, this massive, long-term paradigm shift presented significant challenges for children and parents alike51. Sun et al. indicated that students subjected to the online delivery mode for a long time since the spread of COVID-19 were more likely to fall behind in their grades and be affected by various aspects of psychological stress52. The impact on pupils can easily be passed on to parents to varying degrees, becoming a psychological burden and challenge. However, much of the current research is based on the perspective of educators and children, often ignoring the attitudes and mental health factors of the parents.

In a multifactorial study of the impact of parental attitudes and coping styles on mental health in the context of online delivery during the COVID-19 pandemic, parents’ agreement with online delivery was only 30.24%, and 52.28% adopted positive coping styles in times of stress. We found that the significant factors for parental mental health (measured as PSS, PHQ-9, and GHQ-12 quantitative values) were parental gender, the child’s grade, perceived stress about online classes, whether the child has ADHD, positive or negative coping styles, and subjective attitudes toward supporting online classes. This study separately examined the influence of individual child characteristics and subjective parental attitudes and behavioral patterns on their mental health.

Individual child characteristics

The analysis of children’s ADHD subtypes revealed that the regression coefficients for the quantified values of parental psychological problems were significantly lower for parents of children without ADHD compared to parents of children with mixed ADHD symptoms (Tables 5 and 6). However, it is noteworthy that the regression coefficient was positive when parents disagreed with online classes, which might be due to subjective negative parental attitudes masking the impact of the child with ADHD on the parent’s mental health. ADHD, whose main symptoms are inattention, hyperactivity, and impulsivity, is one of the most common psychiatric disorders, with a prevalence of 4‒6% in children and adolescents53,54 and 5.6% in this study (Table 1). Interestingly, the mental health impact of children with ADHD on their parents was similar across time. One study on the parents of children with ADHD found that such parents were at greater risk of developing psychopathology55. This type of phenomenon showed a more pronounced trend during the COVID-19 pandemic in the context of a long-term home isolation policy and multiple social pressures. The unfamiliarity with electronic devices, concerns about performance in online classes, and the hyperactivity and inattention of children with ADHD symptoms continue to impact parents’ psychological defenses, ultimately leading to a range of mental health problems. Research has shown that the majority of children with ADHD (40‒60%) exhibit both oppositional defiant disorder (ODD) and/or conduct disorder56. This oppositional psychology or behavior toward elders was often amplified in the online mode of instruction during the COVID-19 pandemic due to the long hours spent together.

In addition, this study found significant effects on parents’ mental health when their children were in the middle and upper grades. Interestingly, the higher grades did not increase parental stress but instead increased parental depression and reduced the general psychological state of GHQ-12 scores of the parents (Tables 5 and 6). In a study on the consistency of parental anxiety with children, it was reported that in 76% of the parents in the sample who suffered from depressive psychological problems, the children tended also to have some mental health problems. This was often an indirect link, and this effect was not found in younger children57. In the context of the COVID-19 pandemic, the online mode of delivery led to varying degrees of adaptation and concerns about academic performance, often creating a state of stress and anxiety among students, particularly in the context of the long-term home isolation policy. Often, as students progress through the grades, the burden of schoolwork and the pressure to progress to higher education intensifies this phenomenon. Parents who either respond positively or negatively to online classes, with some threat to employment, salary packages, etc., all expressed concerns about online classes, especially when their children were at some critical point in their senior years. Several studies have found that the correlation between psychological problems such as parental and child depression increases with the child’s age58,59. Even parents who have some reservations about the online mode of instruction for their younger children show some tolerance and emotion compared to their attitudes toward the online instruction of their middle and upper school children, contributing to a relatively relaxed state of mind and psychological situation. Although parents who experience depression and other psychological states in the face of heavy academic pressure on their children in the upper grades are often less able to cope with the difficulties of the upper grades and the gradual expression of their children’s sense of autonomy, the parental pressure in this situation is not significant60,61.

Parents’ willingness to respond in a subjective way

This study focused on the impact of long-term online teaching of children on parental mental health during the COVID-19 pandemic, specifically regarding parents adopting two distinctly different behavioral approaches, negative or positive. The univariate group comparisons in Table 4 show that parental adoption of positive coping resulted in significantly lower mean scores on the PSS, PHQ-9, and GHQ-12 (109.37–98.66, 11.33–10.11, and 3.17–2.57). During the global spread of the virus, the fear of getting sick inspired more negative emotions dominated by stress, anxiety, and other psychological disorders62. A study in Iran showed that 34.8%, 32.2%, and 29% of the adult population in the Yazd region had negative psychological symptoms such as stress, anxiety, and depression, respectively, during the COVID-19 pandemic63. Health concerns, accompanied by economic stagnation and various aspects of the child’s schooling, have resulted in strong negative psychological feedback from parents during the COVID-19 period, especially in a negative way. With this negative behavioral approach, accompanied by a distrust of electronic devices and online delivery modes, parents often voice doubts about the effectiveness of their children’s lessons. However, the parental pressure and anxiety brought about by this pandemic can easily demotivate students and discourage them from learning64. Children who lose interest in learning will eventually regress in the long-term online mode, and this vicious circle will further feed parents’ frustration with the online mode. As shown in Table 5, in the multiple regression results for parental mental health and its correlates under different coping styles, we found an interesting phenomenon: among parents who maintained a negative coping style, men tended to show high levels of parental stress significance (i.e., a decrease of 2.03 for women compared to men), and the parents with a positive coping style did not show significant gender-differentiated behavior. In addition, the lower the level of stress parents experienced from online classes was, the lower the regression coefficients were for the quantitative values of parental psychological problems. Furthermore, parents who maintained a positive coping style had higher regression coefficients compared to those with negative coping styles (Table 5). Active parental involvement in family education is crucial to the child’s development65, and it is relatively easier to maintain a healthy psychological level in such a benign parenting relationship. Pratama et al. showed through a study of parental feedback in the face of home-based online classes during the pandemic66 that positive parental coping in the face of a long-term online delivery model is an effective safeguard for advancing children’s learning progress, which forms, to some degree, a virtuous cycle.

It is also worth noting, as shown in Table 4, that subjective parental disapproval of online instruction resulted in significantly lower means scores on the PSS, PHQ-9, and GHQ-12 (108.85–102.27, 11.45–10.45, and 3.41–2.66). Thus, in addition to the differential impact of how parents respond to long-term online classes, the subjective willingness or unwillingness to support online classes showed a similarly significant impact. Although some have argued that parental involvement in the child’s learning experience during COVID-19 is unprecedented, the fact is that parental behavioral interference and even subjective attitudes have a profound impact on the child’s learning and have done so for decades67–69. Plowman et al. showed that children’s online learning requires more subjective support and assistance from parents than formal supervision70. Our analysis revealed that when parents reported subjective agreement with online classes, their perception of low stress associated with these classes was more negatively correlated with levels of psychological well-being than when they disagreed with online classes (as rated by the PSS, PHQ, and GHQ) (Table 6). A 2019 study found that parents’ attitudes toward their children’s online learning affected the quality of their children’s learning71 and that children’s learning often feeds back to some extent on the parents’ mental health state. The above phenomenon may be because the expectations of these parents are more aligned with reality. They may have a negative perception of online delivery, so their expectations of its effectiveness are lower. As a result, they may feel less stress and disappointment when reality meets their expectations. Conversely, parents who identify with online delivery may have higher expectations and feel more stress and disappointment when reality does not meet those expectations.

Finally, parents should show a more tolerant and supportive attitude to the online teaching model for students during COVID-19, even in the face of the many stressors that arise at home. With the rapid development of technology and the spread of new electronic devices in modern society, this online mode of delivery will not just be a significant feature of the COVID-19 period but will gradually become more common in the future. A range of theories derived from motivation research, such as self-determination theory72, expectancy-value theory73, and achievement goal theory74, suggest that active involvement in children’s learning processes, with the intervention of a facilitator and the provision of some external motivators, can effectively address the psychological factors that influence learner motivation, engagement, and learning. Therefore, it is only by being more optimistic and accepting of new things and by motivating their children to varying degrees that parents can guide their children to adjust to their mental health.

Strengths & limitations

We analyzed the differences in parental mental health and its correlates across coping styles and attitudes toward online classes and explored how factors such as children with ADHD symptoms, parental attitudes toward online classes, and parental coping styles all affect parental mental health. The results suggest that, at a time when online delivery is becoming socially acceptable, attention must be paid to the risk of mental illness it poses to parents. In addition, policies and interventions need to be developed to increase parental acceptance of online delivery. It is hoped that this study can provide effective guidance on parental mental health issues in the post-pandemic era and the emerging wave of online education. In addition, the results of this study are based on a relatively large sample drawn from a randomized whole group and may partially reflect the mental health characteristics of parents of school-aged children aged 6‒13 years in China between pandemics and, to some extent, represent the sociological characteristics of parents suffering from mental health problems under the pressure of online delivery. The present results may serve as a reference for countries with similar cultural backgrounds.

However, our study has several limitations. First, this study is a cross-sectional survey limited in its ability to provide longitudinal judgments of the development of parental mental disorder characteristics, and longitudinal prospective observations may be considered in the future. In addition, the parenting stress, general mental health, and depression scales included in the survey contain a wide range of behaviors of varying clinical relevance, and some atypical behaviors may not be captured by these scales. Furthermore, there is a significant imbalance in group sizes between children with normal ADHD and those with other types of ADHD, which might affect the generalizability and precision of our findings. This imbalance highlights the need for larger sample sizes for each ADHD subtype in future research. Despite combining all other types of ADHD into one group for analysis, the imbalance remains, and this issue should be addressed in future studies.

Conclusion

Few reports are available on parents’ mental health factors against the background of long-term online teaching during COVID-19. Therefore, in this study, we conducted a multifactorial survey on the impact of parents’ attitudes and coping styles on their mental health in this context. The results showed that during the COVID-19 pandemic in China, facing the long-term online teaching mode, parents of senior children and children with ADHD showed a higher risk of depression and other psychological disorders due to excessive worry and anxiety. In addition, there was a close relationship between parents’ subjective attitudes and behavioral patterns and their mental health status: parents who adopted a positive coping style were relatively less susceptible to the negative impact of both the pandemic and online teaching, so they gradually adapted to their children’s online teaching mode and provided guidance to varying degrees. Subjectively, parents who did not agree with online teaching had a better mental health status compared to parents who agreed with online courses. The findings of this work can provide practical guidance on parental mental health issues in the post-pandemic era and the emerging wave of online education, with a focus on the stresses that online classes induce in parents, improving parental acceptance of online classes and their mental health, and providing further psychological relief measures and health programs for different groups of students and parents.

Acknowledgements

We gratefully acknowledge the support of the Medical Science and Technology Plan Project in Ningbo, Zhejiang Province, China (2020Y22), as well as supports from the Medical and Health Technology of Zhejiang Province, China (2021KY330) and the Ningbo Natural Science Foundation, Zhejiang Province, China (202003N4262).

Author contributions

F.C., L.C., and H.X. conceived and designed the experiments. C.W. and R.D. performed the experiments and collected data. D.C. and J.L. analyzed the data. H.Y. and L.L. contributed to the writing of the manuscript and provided critical revisions that addressed important intellectual content. All authors discussed the results and implications and commented on the manuscript at all stages. *H.Y. and L.L. also served as corresponding authors, overseeing the project coordination and ensuring the integrity of the work from inception to published article. F.C. and L.C. contributed equally to this work due to L.C. significant contributions in completing the revisions.

Data availability

The data that support the findings of this study are available on request from the corresponding author, [Liu], upon reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval

This survey was conducted following the principles of the Declaration of Helsinki. The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers): The Ningbo Kangning Hospital granted Ethical approval to carry out the study within its facilities (Ethical Application Ref. No.: NBKNYY-2021-LC-1, 2021.3.15–2024.3.14).

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Fang Cheng and Lixian Chen.
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References

1. Zhu N Zhang DY Wang WL A novel corona-virus from patients with pneumonia in China, 2019 N. Engl. J. Med. 2020 382 8 727 733 10.1056/NEJMoa2001017 31978945
Zhu, N. et al. A novel corona-virus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382(8), 727–733. 10.1056/NEJMoa2001017 (2020).31978945 10.1056/NEJMoa2001017
2. Drosten C Günther S Preiser W Identification of a novel coronavirus in patients with severe acute respir-atory syndrome N. Engl. J. Med. 2003 348 20 1967 1976 10.1056/NEJMoa030747 12690091
Drosten, C. et al. Identification of a novel coronavirus in patients with severe acute respir-atory syndrome. N. Engl. J. Med. 348(20), 1967–1976. 10.1056/NEJMoa030747 (2003).12690091 10.1056/NEJMoa030747
3. Ksiazek TG Erdman D Goldsmith CS A novel coronavirus associated with severe acute respiratory syndrome N. Engl. J. Med. 2003 348 20 1953 1966 10.1056/NEJMoa030781 12690092
Ksiazek, T. G. et al. A novel coronavirus associated with severe acute respiratory syndrome. N. Engl. J. Med. 348(20), 1953–1966. 10.1056/NEJMoa030781 (2003).12690092 10.1056/NEJMoa030781
4. Zaki AM van Boheemen S Bestebroer TM Isola-tion of a novel coronavirus from a man with pneumonia in Saudi Arabia N. Engl. J. Med. 2012 367 19 1814 1820 10.1056/NEJMoa1211721 23075143
Zaki, A. M. et al. Isola-tion of a novel coronavirus from a man with pneumonia in Saudi Arabia. N. Engl. J. Med. 367(19), 1814–1820. 10.1056/NEJMoa1211721 (2012).23075143 10.1056/NEJMoa1211721
5. World Health Organization, 2020. Coronavirus disease 2019 (COVID-19). Situation Report-55. World Health Organ-ization. https://www.who.int/emergencies/diseases/novel- coronavirus-2019/situation-reports [Accessed on Mar. 16, 2020].
6. Fry K E-learning markets and providers: Some issues and prospects Educ. Train. 2001 43 4/5 233 239 10.1108/EUM0000000005484
Fry, K. E-learning markets and providers: Some issues and prospects. Educ. Train. 43(4/5), 233–239. 10.1108/EUM0000000005484 (2001).10.1108/EUM0000000005484
7. Hrastinski S Asynchronous and synchronous e-learning Educ. Quart. 2008 31 4 51 55
Hrastinski, S. Asynchronous and synchronous e-learning. Educ. Quart. 31(4), 51–55 (2008).
8. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies.
9. Tallent-Runnels MK Thomas JA Lan WY Cooper S Ahern TC Shaw SM Liu X Teaching courses online: A review of the research Rev. Educ. Res. 2006 76 1 93 135 10.3102/00346543076001093
Tallent-Runnels, M. K. et al. Teaching courses online: A review of the research. Rev. Educ. Res. 76(1), 93–135. 10.3102/00346543076001093 (2006).10.3102/00346543076001093
10. Joshi O Chapagain B Kharel G Poudyal NC Murray BD Mehmood SR Benefits and challenges of online instruction in agriculture and natural resource education Interact. Learn. Environ. 2020 10.1080/10494820.2020.1725896
Joshi, O. et al. Benefits and challenges of online instruction in agriculture and natural resource education. Interact. Learn. Environ.10.1080/10494820.2020.1725896 (2020).10.1080/10494820.2020.1725896
11. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, ( March 27, 2020).
12. Bozkurt A Sharma RC Emergency remote teaching in a time of global crisis due to Corona Virus pandemic Asian J. Dist. Educ. 2020 15 1 i iv
Bozkurt, A. & Sharma, R. C. Emergency remote teaching in a time of global crisis due to Corona Virus pandemic. Asian J. Dist. Educ. 15(1), i–iv (2020).
13. Branch RM Dousay TA Survey of instructional development models 2015 5 UK Association for Educational Communications and Technology
Branch, R. M. & Dousay, T. A. Survey of instructional development models 5th edn. (Association for Educational Communications and Technology, UK, 2015).
14. Vlachopoulos D COVID-19: Threat or opportunity for online education High. Learn. Res. Commun. 2020 10 1 2
Vlachopoulos, D. COVID-19: Threat or opportunity for online education. High. Learn. Res. Commun. 10(1), 2 (2020).
15. Tomasik MJ Helbling LA Moser U Educational gains of in-person vs. distance learning in primary and secondary schools: A natural experiment during the COVID-19 pandemic school closures in Switzerland Int. J. Psychol. 2020 56 566 576 10.1002/ijop.12728 33236341
Tomasik, M. J., Helbling, L. A. & Moser, U. Educational gains of in-person vs. distance learning in primary and secondary schools: A natural experiment during the COVID-19 pandemic school closures in Switzerland. Int. J. Psychol. 56, 566–576. 10.1002/ijop.12728 (2020).33236341 10.1002/ijop.12728
16. Blume F Schmidt A Kramer AC Schmiedek F Neubauer AB Homeschooling during the SARS-CoV-2 pandemic: the role of students’ trait self-regulation and task attributes of daily learning tasks for students’ daily self-regulation Z. Erzieh. 2021 24 367 391 10.1007/s11618-021-01011-w
Blume, F., Schmidt, A., Kramer, A. C., Schmiedek, F. & Neubauer, A. B. Homeschooling during the SARS-CoV-2 pandemic: the role of students’ trait self-regulation and task attributes of daily learning tasks for students’ daily self-regulation. Z. Erzieh. 24, 367–391. 10.1007/s11618-021-01011-w (2021).10.1007/s11618-021-01011-w
17. Manca S Delfino M Adapting educational practices in emergency remote education: continuity and change from a student perspective Br. J. Educ. Technol. 2021 2021 1 20 10.1111/bjet.13098
Manca, S. & Delfino, M. Adapting educational practices in emergency remote education: continuity and change from a student perspective. Br. J. Educ. Technol. 2021, 1–20. 10.1111/bjet.13098 (2021).10.1111/bjet.13098
18. Bonal X González S The impact of lockdown on the learning gap: Family and school divisions in times of crisis Int. Rev. Educ. 2020 66 635 655 10.1007/s11159-020-09860-z 32952208
Bonal, X. & González, S. The impact of lockdown on the learning gap: Family and school divisions in times of crisis. Int. Rev. Educ. 66, 635–655. 10.1007/s11159-020-09860-z (2020).32952208 10.1007/s11159-020-09860-z
19. Savage MJ James R Magistro D Donaldson J Healy LC Nevill M Hennis PJ Mental health and movement behaviour during the COVID-19 pandemic in UKuniversity students: Pro-spective cohort study Ment. Health Phys. Act. 2020 19 100357 10.1016/j.mhpa.2020.100357
Savage, M. J. et al. Mental health and movement behaviour during the COVID-19 pandemic in UKuniversity students: Pro-spective cohort study. Ment. Health Phys. Act. 19, 100357 (2020).10.1016/j.mhpa.2020.100357
20. Khan AH Sultana MS Hossain S Hasan MT Ahmed HU Sikder MT The impact of COVID-19 pandemic on mentalhealth & wellbeing among home-quarantined Bangladeshi students: Across-sectional pilot study J. Affect. Disord. 2020 277 121 128 10.1016/j.jad.2020.07.135 32818775
Khan, A. H. et al. The impact of COVID-19 pandemic on mentalhealth & wellbeing among home-quarantined Bangladeshi students: Across-sectional pilot study. J. Affect. Disord. 277, 121–128 (2020).32818775 10.1016/j.jad.2020.07.135
21. Jiang R Knowledge, attitudes and mental health of university stu-dents during the COVID-19 pandemic in China Child. Youth Serv. Rev. 2020 119 105494 10.1016/j.childyouth.2020.105494 33518860
Jiang, R. Knowledge, attitudes and mental health of university stu-dents during the COVID-19 pandemic in China. Child. Youth Serv. Rev. 119, 105494 (2020).33518860 10.1016/j.childyouth.2020.105494
22. Essadek A Rabeyron T Mental health of French students during the Covid-19 pandemic J. Affect. Disord. 2020 277 392 393 10.1016/j.jad.2020.08.042 32861840
Essadek, A. & Rabeyron, T. Mental health of French students during the Covid-19 pandemic. J. Affect. Disord. 277, 392–393 (2020).32861840 10.1016/j.jad.2020.08.042
23. Kaparounaki CK Patsali ME Mousa DPV Papadopoulou EV Papadopoulou KK Fountoulakis KN University students'mental health amidst the COVID-19 quarantine in Greece Psychiatry Res. 2020 290 113111 10.1016/j.psychres.2020.113111 32450416
Kaparounaki, C. K. et al. University students’mental health amidst the COVID-19 quarantine in Greece. Psychiatry Res. 290, 113111 (2020).32450416 10.1016/j.psychres.2020.113111
24. Copeland WE McGinnis E Bai Y Adams Z Nardone H Devadanam V Hudziak JJ Impact of COVID on collegestudent mental health and wellness J. Am. Acad. Child Adolesc. Psychiatry 2020 60 1 134 141 10.1016/j.jaac.2020.08.466 33091568
Copeland, W. E. et al. Impact of COVID on collegestudent mental health and wellness. J. Am. Acad. Child Adolesc. Psychiatry 60(1), 134–141 (2020).33091568 10.1016/j.jaac.2020.08.466
25. Soykan E Views of students’, teachers’ and parents on the tablet computer usage in education Cypriot J. Educ. Sci. 2015 10 3 228 228 10.18844/cjes.v1i1.68
Soykan, E. Views of students’, teachers’ and parents on the tablet computer usage in education. Cypriot J. Educ. Sci. 10(3), 228–228 (2015).10.18844/cjes.v1i1.68
26. Wößmann L Freundl V Grewenig E Lergetporer P Zierow L Werner K Bildung in der Coronakrise: Wie haben die Schulkinder Die Zeit der Schulschließungen Verbracht, und welche Bildungsmaßnahmen Befürworten die Deutschen? Ifo Schnelldienst 2020 73 25 39
Wößmann, L. et al. Bildung in der Coronakrise: Wie haben die Schulkinder Die Zeit der Schulschließungen Verbracht, und welche Bildungsmaßnahmen Befürworten die Deutschen?. Ifo Schnelldienst 73, 25–39 (2020).
27. Pelikan ER Lüftenegger M Holzer J Korlat S Spiel C Schober B Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination for perceived competence Z. Erzieh. 2021 24 393 418 10.1007/s11618-021-01002-x
Pelikan, E. R. et al. Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination for perceived competence. Z. Erzieh. 24, 393–418. 10.1007/s11618-021-01002-x (2021).10.1007/s11618-021-01002-x
28. Hale L Troxel W Buysse DJ Sleep health: An opportunity for public health to address health equity Annu. Rev. Public Health 2020 41 1 81 99 10.1146/annurev-publhealth-040119-094412 31900098
Hale, L., Troxel, W. & Buysse, D. J. Sleep health: An opportunity for public health to address health equity. Annu. Rev. Public Health 41(1), 81–99 (2020).31900098 10.1146/annurev-publhealth-040119-094412
29. Jiao WY Wang LN Liu J Fang SF Jiao FY Pettoello-Mantovani M Behavioral and emotional disorders in children during the COVID-19 epidemic J. Pediatr. 2020 221 264 266 10.1016/j.jpeds.2020.03.013 32248989
Jiao, W. Y. et al. Behavioral and emotional disorders in children during the COVID-19 epidemic. J. Pediatr. 221, 264–266. 10.1016/j.jpeds.2020.03.013 (2020).32248989 10.1016/j.jpeds.2020.03.013
30. Hai T Swansburg R MacMaster FP Lemay JF Impact of COVID-19 on educational services in Canadian children with attention-deficit/hyperactivity disorder Front. Educ. 2021 6 614181 10.3389/feduc.2021.614181
Hai, T., Swansburg, R., MacMaster, F. P. & Lemay, J. F. Impact of COVID-19 on educational services in Canadian children with attention-deficit/hyperactivity disorder. Front. Educ. 6, 614181. 10.3389/feduc.2021.614181 (2021).10.3389/feduc.2021.614181
31. Tessarollo V Scarpellini F Costantino I Cartabia M Canevini MP Bonati M Distance learning in children with and without ADHD: A case-control study during the COVID-19 pandemic J. Attent. Disord. 2022 26 902 914 10.1177/10870547211027640
Tessarollo, V. et al. Distance learning in children with and without ADHD: A case-control study during the COVID-19 pandemic. J. Attent. Disord. 26, 902–914. 10.1177/10870547211027640 (2022).10.1177/10870547211027640
32. Hasler-Waters L Menchaca MP Borup J Kennedy K Ferdig RE Parental Involvement in K-12 Online and Blended Learning Handbook of Research on K-12 Online and Blended Learning 2014 ETC Press 303 324
Hasler-Waters, L., Menchaca, M. P. & Borup, J. Parental Involvement in K-12 Online and Blended Learning. In Handbook of Research on K-12 Online and Blended Learning (eds Kennedy, K. & Ferdig, R. E.) 303–324 (ETC Press, 2014).
33. Killgore WDS Cloonan SA Taylor EC Dailey NS Loneliness: A signature mental health concern in the era of COVID-19 Psychiatry Res. 2020 290 113117 10.1016/j.psychres.2020.113117 32480121
Killgore, W. D. S., Cloonan, S. A., Taylor, E. C. & Dailey, N. S. Loneliness: A signature mental health concern in the era of COVID-19. Psychiatry Res. 290, 113117 (2020).32480121 10.1016/j.psychres.2020.113117
34. Xin M Luo S She R Negative cognitive and psychological correlates of mandatory quarantine during the initial COVID-19 outbreak in China Am. Psychol. 2020 75 5 607 617 10.1037/amp0000692 32673008
Xin, M. et al. Negative cognitive and psychological correlates of mandatory quarantine during the initial COVID-19 outbreak in China. Am. Psychol. 75(5), 607–617 (2020).32673008 10.1037/amp0000692
35. Holt-Lunstad J Smith TB Baker M Harris T Stephenson D Loneliness and social isolation as risk factors for mortality: A meta-analytic review Perspect. Psychol. Sci. 2015 10 2 227 237 10.1177/1745691614568352 25910392
Holt-Lunstad, J., Smith, T. B., Baker, M., Harris, T. & Stephenson, D. Loneliness and social isolation as risk factors for mortality: A meta-analytic review. Perspect. Psychol. Sci. 10(2), 227–237 (2015).25910392 10.1177/1745691614568352
36. Tian H Liu Y Li Y An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China Science 2020 368 6491 638 642 10.1126/science.abb6105 32234804
Tian, H. et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368(6491), 638–642 (2020).32234804 10.1126/science.abb6105
37. Xie Z, Yang J. Autonomous learning of elementary students at home during the COVID-19 epidemic: A case study of the second elementary school in Daxie, Ningbo, Zhejiang Province, China. Ningbo, Zhejiang Province, China (March 15, 2020), 2020.
38. Zhang W Wang Y Yang L Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak J. Risk Financ. Manag. 2020 13 3 55 10.3390/jrfm13030055
Zhang, W. et al. Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak. J. Risk Financ. Manag. 13(3), 55 (2020).10.3390/jrfm13030055
39. Hair JF Black WC Babin BJ Multivariate Data Analysis 2010 7 Pearson Prentice Hall
Hair, J. F. et al. Multivariate Data Analysis 7th edn. (Pearson Prentice Hall, 2010).
40. Nunnally JC Bernstein IH The assessment of reliability Psychometric Theory 1994 3 248 292
Nunnally, J. C. & Bernstein, I. H. The assessment of reliability. Psychometric Theory 3, 248–292 (1994).
41. Bartlett JE Kotrlik JW Higgins CC Organizational research: Determining appropriate sample size in survey research Inf. Technol. Learn. Perform. J. 2001 19 43 50
Bartlett, J. E., Kotrlik, J. W. & Higgins, C. C. Organizational research: Determining appropriate sample size in survey research. Inf. Technol. Learn. Perform. J. 19, 43–50 (2001).
42. Gau SSF Shang CY Liu SK Lin CH Swanson JM Liu YC Tu CL Psychometric properties of the Chinese version of the Swanson, Nolan, and Pelham, version IV scale–parent form Int. J. Methods Psychiatric Res. 2008 17 1 35 44 10.1002/mpr.237
Gau, S. S. F. et al. Psychometric properties of the Chinese version of the Swanson, Nolan, and Pelham, version IV scale–parent form. Int. J. Methods Psychiatric Res. 17(1), 35–44. 10.1002/mpr.237 (2008).10.1002/mpr.237
43. Qin Z Liang L Research and analysis on the optimal cutoff value of Chinese version PHQ-9 for screening depression in different populations J. Clin. Digest. Dis. 2019 31 5 4
Qin, Z. et al. Research and analysis on the optimal cutoff value of Chinese version PHQ-9 for screening depression in different populations. J. Clin. Digest. Dis. 31(5), 4 (2019).
44. Wang W Ding L Liao Z The optimal cutoff values and screening characteristics of different scoring methods in 12 general health questionnaires Chin. J. Psychiatry 2012 45 6 5
Wang, W. et al. The optimal cutoff values and screening characteristics of different scoring methods in 12 general health questionnaires. Chin. J. Psychiatry 45(6), 5 (2012).
45. Qin X Tang C Zhu S A study on parental stress and related factors in mothers of children with autism Chin. J. Ment. Health 2009 23 009 629 633
Qin, X. et al. A study on parental stress and related factors in mothers of children with autism. Chin. J. Ment. Health 23(009), 629–633 (2009).
46. Wang C Pan R Wan X Tan Y Xu L McIntyre RS Choo FN Tran B Ho R Sharma VK A longitudinal study on the mental health of general population during the COVID-19 epidemic in China Brain Behav. Immun. 2020 87 40 48 10.1016/j.bbi.2020.04.028 32298802
Wang, C. et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav. Immun. 87, 40–48. 10.1016/j.bbi.2020.04.028 (2020).32298802 10.1016/j.bbi.2020.04.028
47. González-Estrada E Cosmes W Shapiro–Wilk test for skew normal distributions based on data transformations J. Statist. Comput. Simul. 2019 89 17 3258 3272 10.1080/00949655.2019.1658763
González-Estrada, E. & Cosmes, W. Shapiro–Wilk test for skew normal distributions based on data transformations. J. Statist. Comput. Simul. 89(17), 3258–3272 (2019).10.1080/00949655.2019.1658763
48. Lyerly SB The average Spearman rank correlation coefficient Psychometrika 1952 17 4 421 428 10.1007/BF02288917
Lyerly, S. B. The average Spearman rank correlation coefficient. Psychometrika 17(4), 421–428 (1952).10.1007/BF02288917
49. Kumari T Shukla V Covid-19: Towards confronting an unprecedented pandemic Int. J. Biol. Innov. 2020 2 1 1 10 10.46505/IJBI.2020.2101
Kumari, T. & Shukla, V. Covid-19: Towards confronting an unprecedented pandemic. Int. J. Biol. Innov. 2(1), 1–10. 10.46505/IJBI.2020.2101 (2020).10.46505/IJBI.2020.2101
50. Chakraborty I Maity P COVID-19 outbreak: Migration, effects on society, global environment and prevention Sci. Total Environ. 2020 728 138882 10.1016/j.scitotenv.2020.138882 32335410
Chakraborty, I. & Maity, P. COVID-19 outbreak: Migration, effects on society, global environment and prevention. Sci. Total Environ. 728, 138882 (2020).32335410 10.1016/j.scitotenv.2020.138882
51. Adedoyin OB Soykan E Covid-19 pandemic and online learning: the challenges and opportunities Interact. Learn. Environ. 2020 10.1080/10494820.2020.1813180
Adedoyin, O. B. & Soykan, E. Covid-19 pandemic and online learning: the challenges and opportunities. Interact. Learn. Environ.10.1080/10494820.2020.1813180 (2020).10.1080/10494820.2020.1813180
52. Sun, S. (2020, August 22). Rural primary education in Cambodia during the pandemic: Challenges and solutions. Cambodian Education Forum. https://cefcambodia.com/2020/08/22/rural-primary-education-in-cambodia-during-the-pandemic-challenges-and-solutions/.
53. Brown RT Freeman WS Perrin JM Stein MT Amler RW Feldman HM PierceK WML Prevalence and assessment of attention-deficit/hyperactivity disorder in primary settings Pediatrics. 2001 107 43 10.1542/peds.107.3.e43
Brown, R. T. et al. Prevalence and assessment of attention-deficit/hyperactivity disorder in primary settings. Pediatrics. 107, 43 (2001).10.1542/peds.107.3.e43
54. Scahill L Schwab-Stone M Epidemiology of ADHD in school-age children Child. Adolesc. Psychiatr Clin. N. Am. 2000 9 541 555 10.1016/S1056-4993(18)30106-8 10944656
Scahill, L. & Schwab-Stone, M. Epidemiology of ADHD in school-age children. Child. Adolesc. Psychiatr Clin. N. Am. 9, 541–555 (2000).10944656 10.1016/S1056-4993(18)30106-8
55. Agha SS Zammit S Thapar A Langley K Maternal psychopathology and offspring clinical outcome: A four-year follow-up of boys with ADHD Eur. Child Adolesc. Psychiatry 2016 10.1007/s00787-016-0873-y 27376657
Agha, S. S., Zammit, S., Thapar, A. & Langley, K. Maternal psychopathology and offspring clinical outcome: A four-year follow-up of boys with ADHD. Eur. Child Adolesc. Psychiatry10.1007/s00787-016-0873-y (2016).27376657 10.1007/s00787-016-0873-y
56. August GJ Realmunto GM MacDonald AW III Nugent SM Crosby R Prevalence of ADHD and comorbid disorders among elementary school children screened for disruptive behavior J. Abnormal Child. Psychol. 1996 24 571 595 10.1007/BF01670101
August, G. J., Realmunto, G. M., MacDonald, A. W. III., Nugent, S. M. & Crosby, R. Prevalence of ADHD and comorbid disorders among elementary school children screened for disruptive behavior. J. Abnormal Child. Psychol. 24, 571–595 (1996).10.1007/BF01670101
57. Frick PJ Silverthorn P Evans C Assessment of child-hood anxiety using structured interviews: Patterns of agreementamong informants and association with maternal anxiety Psychol. Assess. 1994 6 372 379 10.1037/1040-3590.6.4.372
Frick, P. J., Silverthorn, P. & Evans, C. Assessment of child-hood anxiety using structured interviews: Patterns of agreementamong informants and association with maternal anxiety. Psychol. Assess. 6, 372–379 (1994).10.1037/1040-3590.6.4.372
58. Edelbrock C Costello AJ Dulcan MK Conover NC Kala R Parent–child agreement on child psychiatricsymptoms via structured interview J. Child. Psychol. Psychiatry 1986 27 181 190 10.1111/j.1469-7610.1986.tb02282.x 3958075
Edelbrock, C., Costello, A. J., Dulcan, M. K., Conover, N. C. & Kala, R. Parent–child agreement on child psychiatricsymptoms via structured interview. J. Child. Psychol. Psychiatry 27, 181–190 (1986).3958075 10.1111/j.1469-7610.1986.tb02282.x
59. Verhulst FC Althaus M Berden FMG The child assessment schedule: Parent child agreement and validity measures J. Child. Psychol. Psychiatry 1987 28 455 466 10.1111/j.1469-7610.1987.tb01766.x 3597567
Verhulst, F. C., Althaus, M. & Berden, F. M. G. The child assessment schedule: Parent child agreement and validity measures. J. Child. Psychol. Psychiatry 28, 455–466 (1987).3597567 10.1111/j.1469-7610.1987.tb01766.x
60. Nye C Turner H Schwartz J Approaches to parent involvement for improving the academic performance of elementary school age children Campbell Syst. Rev. 2006 2 1 1 49 10.4073/csr.2006.4
Nye, C., Turner, H. & Schwartz, J. Approaches to parent involvement for improving the academic performance of elementary school age children. Campbell Syst. Rev. 2(1), 1–49. 10.4073/csr.2006.4 (2006).10.4073/csr.2006.4
61. Eccles JS Harold RD Parent-school involvement during the early adolescent years Teach. Coll. Record 1993 94 3 568 658 10.1177/016146819309400311
Eccles, J. S. & Harold, R. D. Parent-school involvement during the early adolescent years. Teach. Coll. Record 94(3), 568–658 (1993).10.1177/016146819309400311
62. Liu Y Gayle AA Wilder-Smith A Rocklöv J The reproductive number of COVID-19 is higher compared to SARS coronavirus J. Travel Med. 2020 76 71 76
Liu, Y., Gayle, A. A., Wilder-Smith, A. & Rocklöv, J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. 76, 71–76 (2020).
63. Mirzaei M Yasini Ardekani SM Mirzaei M Dehghani A Prevalence of depression, anxiety and stress among adult population: Results of Yazd health study Iran. J. Psychiatry 2019 14 2 137 146 31440295
Mirzaei, M., Yasini Ardekani, S. M., Mirzaei, M. & Dehghani, A. Prevalence of depression, anxiety and stress among adult population: Results of Yazd health study. Iran. J. Psychiatry 14(2), 137–146 (2019).31440295
64. Pekrun R Lichtenfeld S Marsh HW Murayama K Goetz T Achievement emotions and academic performance: Longitudinal models of reciprocal effects Child Dev. 2017 88 5 1653 1670 10.1111/cdev.12704 28176309
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K. & Goetz, T. Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Dev. 88(5), 1653–1670 (2017).28176309 10.1111/cdev.12704
65. Kellaghan T Sloane K Alvarez B Bloom BS The Home Environment and School Learning: Promoting Parental Involvement in the Education of Children 1993 Jossey-Bass
Kellaghan, T., Sloane, K., Alvarez, B. & Bloom, B. S. The Home Environment and School Learning: Promoting Parental Involvement in the Education of Children (Jossey-Bass, 1993).
66. Pratama AR Firmansyah FM Disengaged, positive, or negative: Parents’ attitudes toward learning from home amid COVID-19 pandemic J. Child. Fam. Stud. 2021 30 7 1803 1812 10.1007/s10826-021-01982-8 34035641
Pratama, A. R. & Firmansyah, F. M. Disengaged, positive, or negative: Parents’ attitudes toward learning from home amid COVID-19 pandemic. J. Child. Fam. Stud. 30(7), 1803–1812 (2021).34035641 10.1007/s10826-021-01982-8
67. Fishel M Ramirez L Evidence-based parent involvement interventions with school-aged children School Psychol. Quart. 2005 20 4 371 10.1521/scpq.2005.20.4.371
Fishel, M. & Ramirez, L. Evidence-based parent involvement interventions with school-aged children. School Psychol. Quart. 20(4), 371. 10.1521/scpq.2005.20.4.371 (2005).10.1521/scpq.2005.20.4.371
68. Gross D Bettencourt AF Taylor K Francis L Bower K Singleton DL What is parent engagement in early learning? Depends who you ask J. Child. Fam. Stud. 2020 29 3 747 760 10.1007/s10826-019-01680-6
Gross, D. et al. What is parent engagement in early learning? Depends who you ask. J. Child. Fam. Stud. 29(3), 747–760. 10.1007/s10826-019-01680-6 (2020).10.1007/s10826-019-01680-6
69. Topor DR Keane SP Shelton TL Calkins SD Parent involvement and student academic performance: a multiple mediational analysis J. Prevent. Intervent. Commun. 2010 38 3 183 197 10.1080/10852352.2010.486297
Topor, D. R., Keane, S. P., Shelton, T. L. & Calkins, S. D. Parent involvement and student academic performance: a multiple mediational analysis. J. Prevent. Intervent. Commun. 38(3), 183–197. 10.1080/10852352.2010.486297 (2010).10.1080/10852352.2010.486297
70. Plowman L Stephen C Children, play, and computers in pre-school education Br. J. Educ. Technol. 2005 36 2 145 157 10.1111/j.1467-8535.2005.00449.x
Plowman, L. & Stephen, C. Children, play, and computers in pre-school education. Br. J. Educ. Technol. 36(2), 145–157. 10.1111/j.1467-8535.2005.00449.x (2005).10.1111/j.1467-8535.2005.00449.x
71. Erdogan NI Johnson JE Dong PI Qiu Z Do parents prefer digital play? Examination of parental preferences and beliefs in four nations Early Childh. Educ. J. 2019 47 131 142 10.1007/s10643-018-0901-2
Erdogan, N. I., Johnson, J. E., Dong, P. I. & Qiu, Z. Do parents prefer digital play? Examination of parental preferences and beliefs in four nations. Early Childh. Educ. J. 47, 131–142. 10.1007/s10643-018-0901-2 (2019).10.1007/s10643-018-0901-2
72. Ryan RM Deci EL Intrinsic and extrinsic motivation from a self-determination theory perspective definitions, theory, practices, and future directions Contemp. Educ. Psychol. 2020 10.1016/j.cedpsych.2020.101860
Ryan, R. M. & Deci, E. L. Intrinsic and extrinsic motivation from a self-determination theory perspective definitions, theory, practices, and future directions. Contemp. Educ. Psychol.10.1016/j.cedpsych.2020.101860 (2020).10.1016/j.cedpsych.2020.101860
73. Wigfield A Eccles JS Expectancy–value theory of achievement motivation Contemp. Educ. Psychol. 2000 25 1 68 81 10.1006/ceps.1999.1015 10620382
Wigfield, A. & Eccles, J. S. Expectancy–value theory of achievement motivation. Contemp. Educ. Psychol. 25(1), 68–81 (2000).10620382 10.1006/ceps.1999.1015
74. Senko C Hulleman CS Harackiewicz JM Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions Educ. Psychol. 2011 46 1 26 47 10.1080/00461520.2011.538646
Senko, C., Hulleman, C. S. & Harackiewicz, J. M. Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions. Educ. Psychol. 46(1), 26–47 (2011).10.1080/00461520.2011.538646
