
==== Front
BMC Psychol
BMC Psychol
BMC Psychology
2050-7283
BioMed Central London

39252144
1922
10.1186/s40359-024-01922-3
Research
Psychometric evaluation of a Spanish translation of the moral injury symptom scale for healthcare professionals
http://orcid.org/0000-0001-5792-9847
Cabanillas-Chavez María Teresa maritere@upeu.edu.pe

1
White Michael 2
Medina-Bacalla Willy Jhon 1
Arévalo-Ipanaqué Janet Mercedes 1
Zegarra Roxana Obando 3
Suyo-Vega Josefina Amanda 4
Morales-García Mardel 1
Morales-García Wilter C. 1
Taylor Elizabeth Johnston 5
1 https://ror.org/042gckq23 grid.441893.3 0000 0004 0542 1648 Graduate Office of Health Sciences, School of Graduate Studies, Universidad Peruana Unión, Lima, Peru
2 https://ror.org/042gckq23 grid.441893.3 0000 0004 0542 1648 Graduate Office of Psychology, School of Graduate Studies, Universidad Peruana Unión, Lima, Peru
3 https://ror.org/03yczjf25 grid.11100.31 0000 0001 0673 9488 Hospital Daniel Alcides Carrión, Universidad Peruana Cayetano Heredia, Lima, Peru
4 https://ror.org/0297axj39 grid.441978.7 0000 0004 0396 3283 Universidad Cesar Vallejo, Lima, Peru
5 https://ror.org/04bj28v14 grid.43582.38 0000 0000 9852 649X School of Nursing, Loma Linda University, West Hall, 11262 Campus St, Loma Linda, CA USA
2 9 2024
2 9 2024
2024
12 4684 10 2023
23 7 2024
© The Author(s) 2024
2024
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Background

Moral injury is prevalent among health care professionals, especially nurses. It can have negative personal consequences for clinicians, and indirectly impact the quality of patient care. Although nurses around the world experienced moral injury during the pandemic, it will continue to be a professional challenge. Thus, this study aimed to determine the psychometric properties of a scale measuring moral injury translated into Spanish.

Methods

A methodological study with a cross-sectional approach was conducted. After translating the Moral Injury Symptom Scale for Healthcare Professionals (MISS-HP) into Peruvian Spanish (MISS-HP-S) using International Test Commission methods, data were collected using online survey methods from a sample of 720 Peruvian nurses. Analytical methods included exploratory and confirmatory factor analysis, and invariance by age were examined. The corrected homogeneity index, ordinal alpha, and McDonald’s omega allowed the evaluation of internal reliability.

Results

Findings from this sample of nurses who were mostly female (92%), from coastal Peru (57%), and averaged 39 (± 11) years of age, provided support for the validity and reliability of the MISS-HP-S. Structural validity was endorsed by findings indicating consistent factorial structure and adequate invariance among different age groups. In this study, three factors were observed: guilt/shame, condemnation, and spiritual strength. Internal consistency values included an ordinal alpha of 0.795 and McDonald’s omega of 0.835.

Conclusion

These findings differ from those reported from previous studies in other cultural contexts, suggesting the influence of cultural and sample-specific factors in the perception of moral injury among Peruvian nurses. Because this evidence supports the validity of the MISS-HP-S, it can be used in professional practice and in future research to identify and address situations that contribute to nurse moral injury.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-024-01922-3.

Keywords

Moral injury
Nurses
Psychometrics
Statistical factor analysis
Posttraumatic stress disorders
Peru
Spanish
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcRecent evidence suggests that moral distress and injury among healthcare workers may be common [1]. The COVID-19 pandemic created novel and potentially traumatic moral and ethical challenges, placing many health care workers at risk for moral injury [2]. For example, nurses were often involved in patient care at decisive moments when decisions were made about how to prioritize care for critically ill patients [3]. This moral distress and injury is not isolated to First World countries, but is also experienced by nurses and other clinicians around the globe [4].

Moral injury, however, existed among nurses long before the global pandemic and will presumably continue to be a professional challenge after the pandemic subsides. Although moral injury is not a mental illness, it commonly co-occurs with anxiety and depression [5]. Indeed, guilt, anger, spiritual distress, and suicidal ideation were observed to be associated with moral injury among healthcare professionals [3]. Given the global prevalence and impact of moral injury, it is vital to have a psychometrically robust measure for it that is available in diverse languages and cultures. Thus, this study examined the validity and reliability of a scale measuring moral injury that was translated into Spanish for use among Peruvian nurses.

Background

Originally identified during research about military veterans, moral injury is a relatively new construct that applies to healthcare providers [6]. Moral injury is defined as psychological distress resulting from actions, or lack thereof, that violate one’s moral or ethical code [5, 7]. Moral injury has also been described as a pattern of behavior defined by an individual striving to avoid or control his or her moral pain [1]. Victims of moral injury suffer psychological and spiritual harm when they witness or participate in immoral acts or are betrayed [8–13]. Given these definitions of moral injury, it is accepted that the cause of the moral injury must be addressed rather than focusing solely on resolving individual distress [4].

Thus, moral injury is a varied and complex phenomenon that can manifest itself as betrayal, guilt, shame, moral concerns, loss of trust, loss of meaning, difficulty in forgiveness, self-condemnation, religious struggle, and loss of religious faith [8–13].

Moral injury can also cause symptoms of alterations in thoughts and beliefs, pain, meaninglessness [7, 14, 15], hopelessness [16], loss of confidence in religious faith, social alienation, and self-condemnation [17]. These symptoms can lead to low self-esteem, low job satisfaction, burnout, and intention to leave the job or profession [5]. Moral injury can be extraordinarily harmful to mental and spiritual health and can even be life-threatening.

In response to the need to measure moral injury, the Moral Injury Symptom Scale (MISS) was initially developed for U.S. war veterans and included 45 items [18]. Subsequently, it was abbreviated in a short form, with 10 items assessing ten dimensions of moral injury. This shortened version of the MISS demonstrated validity and reliability in a population of 427 American veterans [19].

The MISS-Short Form was adapted and validated for use in health care professionals in 2020, resulting in the MISS-HP scale [20]. The MISS-HP was extensively evaluated in a sample of 181 clinicians, mostly physicians. In addition to evaluating the structural validity, Mantri et al. [20] found support for the convergent and divergent validity when associations were observed in expected directions with variables such as religiosity, burnout, depression. The MISS-HP also demonstrated strong levels of specificity and sensitivity (both above 80%). Subsequently, this scale was validated in several countries, including China, Turkey, and Germany, and showed high levels of reliability and validity [21–23].

The factor structure of the scale, however, differed between the studies. In a California study [20], the MISS-HP scale included three factors: guilt/shame (Items 1,2,3, and 4), spiritual problems (Items 5,6,7 and 10), and condemnation (Items 8 and 9). Studies from China and Turkey [21, 22] likewise demonstrated a three factor structure: shame and guilt (Items 1, 3 and 4), distrust (Items 5, 6 and 10), and forgiveness (Items 7,8 and 9). In the study completed in Germany with the MISS-HP [23], the scale was validated with 9 items instead of 10, and items were grouped into two factors (i.e., Factor I including Items 1, 2, 3, and 4; Factor II including Items 5, 6, 7, 8, and 9); Item 10 was excluded due to its lack of correlation with other items. Using Cronbach’s alphas, each of these studies observed adequate internal reliability for the MISS-HP (i.e., 0.70 [China]; 0.75 [USA]; 0.79 [Germany]; 0.91 [Turkey]).

Despite the prevalence and salience of moral injury, there is a dearth of research on moral injury in health professionals in Latin American countries such as Peru. In addition, the scales available to measure moral injury, such as the MISS-HP, present certain limitations and discrepancies in their psychometric properties and factorial structure [18, 20–23]. Therefore, the aim of this study was to assess the psychometric properties of a Spanish translation of the MISS-HP (MISS-HP-S) scale to measure moral injury among nurses in Peru. The following research questions were addressed: In a sample of Peruvian nurses: (1) What is the structural validity of the MISS-HP-S? (2) What is the internal reliability of the MISS-HP-S? (3) What is the factorial invariance of the MISS-HP-S considering two age groups?

Methods

This psychometric evaluation study followed an instrumental design [24]. It was preliminary work for a larger cross-sectional, observational study investigating how moral injury and other spiritual responses to providing nursing care during the pandemic impacted the nurse outcomes of posttraumatic stress, burnout, job satisfaction, and intent to leave nursing. Thus umbrella study was designed by nurse researchers in the USA and replicated in four countries including Peru.

The study was conducted by an interdisciplinary team of scientists at a Peruvian university and approved by the ethics committee of the School of Graduate Studies of the Universidad Peruana Unión (Ref2021-CE-EPG-000075). All participants were given information about the study including possible risks and benefits and were assured of the confidentiality of their responses and the voluntary nature of their participation before being asked to give a waiver of consent to continue with the questionnaire. Participants were also provided with the name and email address of the lead researcher in case they had any questions or concerns.

Sample, recruitment, and setting

A non-probabilistic convenience sample of nurses from across the nation of Peru was recruited to participate. Inclusion criteria were: Registered Nurse, employed in a nursing role in Peru, and willingness to participate. The sample size was estimated by calculating the number of observed and latent variables to be studied, the anticipated effect size (λ = 0.3), the desired statistical significance (α = 0.05), and the level of statistical power (1 - β = 0.95) [25]. This power analysis determined a minimum sample of 223 participants to be needed.

Multiple methods were used to recruit participants. First, nurse administrators at 12 private, public, and community-based healthcare institutions in Peru dedicated to the care of patients with COVID-19 were engaged to distribute an email study participation invitation to all their nursing staff. Only seven of these institutions (i.e., 6 hospitals and 1 clinic), however, agreed to participate. Second, invitations to participate in the study were also distributed via WhatsApp to established groups of nurses in various clinical specialties employed in private and public healthcare settings. Last, in lieu of a monetary incentive or gift for participation—considered unethical in Peruvian culture, a series of six weekly online seminars about aspects of nurse-provided spiritual care (with continuing education credits provided) were offered to attract participants. That is, potential participants were provided a hotlink in the WhatsApp recruitment so that they could go to a web form to complete the study questionnaire. Those who completed the survey were then given the information about how to attend the online seminar. Also, some participants who attended the first online seminar yet had not completed the survey were given a hotlink in the Zoom chat feature and asked to complete the survey then. The survey site was then closed at the start of the first seminar lecture. As part of the invitation, the participants were informed of the research objective and ethical aspects of the study (e.g., risks, benefits, confidentiality, voluntary participation).

Data collection

Procedure

Data collection occurred between April and June of 2022, when the COVID-19 pandemic was substantially affecting the healthcare system in Peru. Participants gave informed consent before completing the questionnaire by clicking on the “yes” option for a question asking “Are you willing to complete this questionnaire?” The average length of time it took for participants to complete the survey was 6.8 min.

Instruments

The questionnaire that participants completed included the MISS-HP as well as several other scales measuring posttraumatic stress, burnout, intention to leave nursing, job satisfaction, spiritual/religious struggle, posttraumatic growth, and a few demographic items (i.e., age, region of Peru, gender, number of healthcare settings currently employed in, hours worked per month, frequency of care for COVID patients). Together, there were 51 items in the questionnaire for the umbrella study, of which this study was preliminary work.

The Moral Injury Symptom Scale-Healthcare Professional (MISS-HP) was designed for healthcare clinicians in the United States by Mantri et al. [20]. The ten-item scale assesses three dimensions: guilt and shame (Items 1, 3, 4, 2, 8 and 9); spiritual problems (Items 5, 6, 7 and 10), and condemnation (Items 2, 8 and 9). Response options for each item are on a 10-point visual analog scale, ranging from 1 (strongly disagree), 2–4 (mildly disagree), 5–6 (neutral), 7–9 (mildly agree), to 10 (strongly agree).

Scores are summed to obtain a total score ranging from 10 to 100, where higher scores indicate a greater presence of moral injury (MI). ROC analysis indicated that a score of 36 or higher would indicate moral injury. Four items are positively worded (Items 5, 6, 7 and 10), and the remaining six are negatively worded.

Our translation from English to Spanish of the Moral Injury Symptom Scale-HP followed Beaton et al.’s [26] and International Test Commission methods. The original authors of the scale were contacted and gave their permission to translate this instrument. The method entailed the following:

Initial translation of the English MISS-HP was independently translated into Spanish by two bilingual English-Spanish speakers with experience in instrument translation. These two versions were compared and consolidated in a meeting where one of the investigators with experience in instrument translation participated. This process did, however, raise questions that were later presented to a focus group.

The Spanish version was again independently back-translated into English by two additional professional translators in the United States who were not familiar with the MISS-HP.

Next, the bilingual investigator overseeing the translation process reviewed the Spanish and English back-translated versions and determined no additional changes were necessary. Thus, the preliminary version of the MISS-HP-Spanish (MISS-HP-S) was finalized.

The preliminary version was administered to a focus group of 21 practicing health professionals to assess comprehension and readability. This focus group was comprised of department heads and other experienced hospital and clinic staff who were recruited by contact with some of the authors, as they have experience but would not participate in the full study data collection. Given focus group feedback, linguistic changes were made to the preliminary MISS-HP-S. Incoherences identified and address included: wording of the “Neutral” response options to “Neither agree nor disagree”, and using simpler wording for Item 4 and changing a translation for “acted in a way that is at odds with my own morals” to “acted contrary to. . .” (i.e., “Me preocupa haber actuado en contra de mi propia moral o valores”).

Subsequently, the investigative team met to review the various iterations of the scale along with the comments from the focus group. This further refined the MISS-HP-S. For instance, after discussing Focus Group findings, the team decided to revise Item 7 that contains “cared for”—a phrase difficult to translate into Spanish. The finalized item was worded as “I have forgiven myself for what happened to me or what happened to those I cared for” (i.e., “Me he perdonado por lo que me pasó o sucedió a quienes he cuidado.”).

This iteration of the MSS-HP-S, together with the English back-translations, was sent to the original authors for review. There were no changes suggested by the original authors, so it was used in this validation study.

Analysis

Data processing was performed using R version 4.2.1 (2022-06-23 ucrt), and RStudio 2022.07.2 Build 576. The packages used for the analysis were “EGAnet”, “foreign”, “dplyr”, “psych”, “psychometric”, “lavaan”, “semPlot”, and “semTools”. No missing data were found in the responses to the MISS-HP-S items.

Descriptive analyses of the MISS-HP-S were performed by calculating the mean (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${\bar{x}}$$ \end{document}), standard deviation (SD), and other measures of variability; skewness (CA) and kurtosis (K) values were considered to identify the distribution of the data. The item-test correlation [27] and corrected homogeneity index (IHc) evaluated internal consistency, taking as a criterion for item conservation values of IHc > 0.2 [28]. The item-test homogeneity index was calculated using Spearman’s correlation coefficient, because the non-normal distribution of the data and because of ordinal level of measurement ascribed to these semantic differential type response options [29].

To study the factor structure, the sample was divided into two equal parts of 360 participants. Half the sample (n = 360) was used for exploratory graphic analysis and exploratory factor analysis (EFA); the other half (n = 360) was used for confirmatory factor analysis (CFA). The number of factors was determined through the exploratory graphic analysis of the EGAnet package, and the EGA and bootEGA functions were performed to evaluate the factor loadings and the stability of the items. Before conducting the EFA, the Kaiser-Meyer-Olkin and Bartlett’s tests were used to evaluate adequacy of the sample [30]. The method of factor extraction for the EFA was through residual minimums (minres) with PROMAX rotation. For CFA, the weighted least squares with adjusted variance (WLSMV) method was used. For the evaluation of the fit models, the chi-square test (χ2), confirmatory fit index and Tucker-Lewis index (≥ 0.95), the root mean square error of approximation (RMSEA) and the standardized root mean square residuals (≤ 0.05) were considered [31].

Given that the structural validity of the instrument had been previously identified in the American [20], Chinese [21], German [23] and Turkish [22] versions, we conducted an initial CFA before performing EFA. Given that these previously-identified structures were not valid in this Peruvian sample of nurses, we then conducted EFA and CFA. CFA was then employed to confirm the model proposed by the EFA.

Finally, the internal consistency measures were calculated for the factorial models analyzed, and the model with the best fit and consistency index was chosen. Using these factors, factorial invariance considering two age groups (i.e., < 35 and ≥ 35 years) was assessed with increment values less than 0.01 on the comparative fit index (CFI) [32]. The cut-off point of 35 years was selected so as to differentiate between young adults who have less experience in the management of ethical and moral conflicts and middle and older adults who likely present a more mature perspective to address these issues due to their accumulated experiences.

Results

The sample (n = 720) was an average of 39.38 (SD = 11.07) years of age; 92.4% were women, and 7.6% were men. Over half (57.2%) were from the coast, 26% were from the highlands, and 16.8% were from the jungle of Peru.

Preliminary analyses

When confirmatory factor analysis was initially conducted with these Peruvian data using the models proposed by the U.S., Turkish, Chinese, and German samples, the results indicated that none of these three models from the four countries showed a good confirmatory factorial fit (Table 1). Thus, EFA was performed.

Table 1 Confirmatory factor analysis of proposed models in four populations

Model	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}^{2}$$\end{document}	df	CMIN/df	P	CFI	TLI	SRMR	RMSEA	IC 95% RMSEA	
USA/ Turkey	406.21	32	12.69	0	0.916	0.881	0.076	0.128	0.117–0.139	
China	587.12	32	18.35	0	0.875	0.824	0.092	0.155	0.144–0.166	
Germany	727.46	34	21.40	0	0.843	0.793	0.113	0.168	0.158–0.179	
Note: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}^{2}$$\end{document} = Chi squared, df = degrees of freedom, CMIN/df = chi squared over degrees of freedom, P = p value, CFI = comparative fit index, TLI = Tucker-Lewis index, SRMR = standardized root mean square residuals, RMSEA = square root mean square error of approximation

Exploratory analyses were carried out with the first half of the sample (Table 2). Initial descriptive analyses for each of the items of the scale, however, revealed that response distributions were mildly and moderately non-normal in 9 of the 10 items. Given these descriptive findings, the item-test homogeneity index was calculated using Spearman’s correlation coefficient. From the values of Spearman’s correlation coefficient, the standard deviation of the item and the total score, the corrected homogeneity index was estimated. This index suggested the elimination of Item 7 (IHc = 0.169), due to its low contribution to the homogeneity of the instrument.

Table 2 Homogeneity index, item-test correlation, and item descriptions

	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{x}$$\end{document}	s	Me	Min	Max	R	CA	K	Rho	IHc	
MISS1	4.29	2.83	4	1	10	9	0.38	-1.05	0.53	0.351	
MISS2	3.76	2.71	3	1	10	9	0.64	-0.82	0.59	0.433	
MISS3	3.26	2.52	2	1	10	9	0.9	-0.37	0.63	0.496	
MISS4	3.41	2.64	2	1	10	9	0.77	-0.63	0.65	0.519	
MISS5	4.82	2.57	5	1	10	9	0.32	-0.69	0.42	0.251	
MISS6	2.65	2.37	2	1	10	9	1.81	2.66	0.55	0.404	
MISS7	4.62	2.99	4	1	10	9	0.45	-1.06	0.38	0.169	
MISS8	2.41	2.35	1	1	10	9	1.89	2.63	0.62	0.498	
MISS9	2.21	2.19	1	1	10	9	2.16	4.03	0.61	0.493	
MISS10	2.93	2.55	2	1	10	9	1.49	1.34	0.49	0.323	
Note: n = 360, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{x}$$\end{document}=Mean, S = standard deviation, Me = Median, Min = Minimum, Max = Maximum, R = Range, CA = Coefficient of asymmetry, K = Kurtosis, Rho = Spearman correlation coefficient, IHc = Index of corrected homogeneity

Factor estimation and EGA and bootEGA analysis

To estimate the number of factors, as well as the stability and robustness of the dimensionality of the MISS-HP-S, given the discrepancy with the number of factors reported in the literature, EGA and bootEGA were used. BootEGA was performed with 2000 iterations, giving a total of three dimensions (Me = 3, 95% CI = 2.064–3.936), tested in 69.5% of the samples. The stability of the dimension and structural consistency in Factor 1 was 93.5%; in Factor 2, it was 99%; and in Factor 3, it was 69.5%. The average stability of the items in the dimensions was as follows: Factor 1, 76.4%; Factor 2, 99.6%; and, Factor 3, 69.5% (Fig. 1).

Fig. 1 Factor plots from EGA and bootEGA

The results of the 2000 bootEGA samples showed that the best fit of the items was in a 3-factor composition: Factor 1 comprising items 5, 6, 7 and 10; Factor 2 comprising items 2, 3 and 4; and Factor 3 comprising Items 8 and 9 (see Table 3). Item 1 in the bootEGA graph was associated with items 2, 3 and 4, but showed a stability of 6.5%; because of this and its instability and low average factor load, it was not considered in the results of item association to a factor (Table 4).

Table 3 Exploratory factor analysis

	F2	F1	F3	h2	u2	com	MSA	
MISS3	0.950	-0.030	-0.120	0.770	0.230	1.000	0.75	
MISS4	0.700	0.020	0.050	0.530	0.470	1.000	0.80	
MISS2	0.690	-0.030	-0.020	0.450	0.550	1.000	0.82	
MISS1	0.370	0.030	0.080	0.190	0.810	1.100	0.82	
MISS6	0.120	0.670	-0.020	0.470	0.530	1.100	0.71	
MISS7	-0.120	0.620	-0.070	0.370	0.630	1.100	0.68	
MISS10	0.040	0.600	-0.010	0.370	0.630	1.000	0.82	
MISS5	-0.020	0.380	0.060	0.160	0.840	1.100	0.68	
MISS8	-0.040	0.000	0.870	0.720	0.280	1.000	0.78	
MISS9	0.080	-0.010	0.630	0.460	0.540	1.000	0.77	
Global MSA							0.76	
Load	2.01	1.33	1.15					
% var	0.20	0.13	0.11					
% Var. Acum.	0.20	0.33	0.45					
% explain	0.45	0.30	0.26					
Cumulative	0.45	0.74	1.00					
Note: F2,F1,F3 = Factors, h2 = commonality, u2 = singularity, com = complexity of the item, MSA = Measure of Sample Adequacy

Table 4 Average factor loadings and item stability over 2000 iterations

	Item stability	Average factor loadings	
	F1	F2	F3	F4	F1	F2	F3	F4	
MISS6	1.000	0.000	0.000	0.000	0.383	0.038	0.073	NaN	
MISS7	1.000	0.000	0.000	0.000	0.318	-0.046	0.000	NaN	
MISS10	1.000	0.000	0.000	0.000	0.344	0.014	0.035	NaN	
MISS5	0.989	0.000	0.005	0.005	0.191	-0.004	0.018	NaN	
MISS2	0.000	1.000	0.000	0.000	-0.012	0.269	0.071	NaN	
MISS3	0.000	1.000	0.000	0.000	-0.008	0.481	0.114	NaN	
MISS4	0.000	0.990	0.010	0.000	0.028	0.327	0.152	NaN	
MISS8	0.000	0.305	0.695	0.000	0.075	0.187	0.353	NaN	
MISS9	0.000	0.305	0.695	0.000	0.022	0.171	0.354	NaN	
MISS1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	
Note: F = factor, NaN = Not defined

Exploratory factor analysis

To perform a deliberate elimination of items despite having the recommendations of the elimination of items 1 and 7 in the previous analyses, we proceeded to perform the exploratory factor analysis considering all the items of the scale (Table 3). An acceptable sample adequacy index (MSA = 0.76) was evidenced with Bartlett’s test of sphericity (X2 = 902.038, p = 0.000). The exploratory factor model proposed three factors: Factor 1 (Items 5, 6, 7 and 10); Factor 2 (Items 1, 2, 3 and 4); and Factor 3 (Items 8 and 9). It was observed that the factor loading of Item 1 was low (λ = 0.37), as was Item 5 (λ = 0.38). The item adequacy index showed low values for Items 5 and 7, both with an MSA value = 0.68. Similarly, low communalities were observed for Items 1 and 5 (h2 = 0.19, h2 = 0.16, respectively). The total variance explained by the three factors was 45%.

Confirmatory factor analysis

Keeping the bootEGA and EFA findings in mind and desiring to avoid the elimination of items, seven confirmatory models were tested. The model with the overall best fit indexes and reliability required the elimination of Items 1, 5, and 7. This model presented Item 10 with the lowest factor loading (λ = 0.479), while the remaining loadings were at least λ = 0.729 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}^{2}$$\end{document} = 25.77, df = 11, p = 0.007, CMIN/df = 2.34, CFI = 0.992, TLI = 0.985, SRMR = 0.031, RMSEA = 0.061). See Fig. 2.

Fig. 2 Confirmatory factor analysis

Reliability coefficients were calculated for all models, and the model selected had the strong alpha ordinal, omega hierarchical, and omega total values (i.e., Factor 1: αo = 0.602; Factor 2: αo = 0.837; Factor 3: αo = 0.835).

Factorial Invariance

The factorial invariance analysis of the MISS-HP-S was performed according to age, recoding it with a cutoff point at 35 years of age. When comparing the SRMR values in different models (configural, metric, scalar and strict), minimal differences–all less than 0.03, were observed. This guaranteed the absence of bias in the measurement by age group. This is also supported by the absence of differences in ΔRMSEA and ΔCFI values when moving from one model to the other. In addition, the measures of changes in the Δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\chi}^{2}$$\end{document} were not significant (p > 0.05), which reinforces the idea that there is no bias in the measurement between the different age groups evaluated (Table 5).

Table 5 Factorial invariance according to age

	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}^{2}$$\end{document}	Df	SRMR	RMSEA	TLI	CFI	ΔSRMR	ΔRMSEA	ΔCFI	Δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}^{2}$$\end{document}	Δdf	p	
Configural	26.810	34	0.0427	0.000	1.026	1							
Metric	30.210	39	0.0448	0.000	1.028	1	0.002	0.000	0.000	5.000	5	0.416	
Scalar	35.197	44	0.0481	0.000	1.024	1	0.003	0.000	0.000	7.790	5	0.168	
Strict	39.736	52	0.0546	0.000	1.029	1	0.006	0.000	0.000	7.719	8	0.461	
Note: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}^{2}$$\end{document} = Chi squared, df = degrees of freedom, P = p value, CFI = comparative fit index, TLI = Tucker-Lewis index, SRMR = standardized root mean square residuals, RMSEA = root mean square error of approximation, Δ = difference

Discussion

Moral injury in nurses who work long hours in at-risk situations and experience burnout in healthcare systems is an issue of growing concern [33]. To address this concern empirically using quantitative methods, validated and reliability scales are needed. This study provided initial analyses of the psychometric properties of the MISS-HP-S scale in the Peruvian context, given the lack of instruments for this population [20]. The results were compared with the factor models from studies using this same scale in different languages. Based on our findings, we recommend a MISS-HP-S that excludes the original Items 1, 5 and 7, leaving a seven-item scale with strong psychometric properties.

The models of the initial U.S. version of the MISS-HP [20] as well as the versions from China [21], Turkey [22], and Germany [23] could not be successfully replicated with the Peruvian sample. This may be due to cultural differences and sample specificity, as the previous studies included both physicians and nurses whereas the present investigation focused only on nurses. Additionally, a detailed analysis of the factorial configurations of the different versions of the MISS-HP revealed similarities and differences in item grouping. For example, the American and Turkish versions showed greater similarity with the Peruvian sample, in which 9 of the 10 items were grouped in the same way, except for Item 2 [20, 22]. The German version, on the other hand, identified two factors instead of the three suggested in this study, which could be due to differences in the reduction techniques used or the small sample size of 156 [23, 34]. The discrepancy with the Chinese study could be explained by the analysis tools used, as the latter employed principal component analysis instead of exploratory factor analysis [21, 35].

The results of this study also highlighted problems related to the internal consistency and stability of Items 1, 5, and 7, which were deleted. These problems included: the lack of contribution of Item 7 to internal consistency in the correlation analysis and corrected homogeneity index, the low stability of Item 1 evidenced in the exploratory graph analysis, the low factor loadings of Items 1 and 5, the low communalities observed in Items 1 and 5, and the low adequacy index shown by Item 7 in the EFA. These findings led us to further analyze the semantic structure of these items using specification Table [36]. We observed that the behavior problems in all but Item 5 are inherently causal and consequential. (Item 5 is not [i.e., “Most of the people I work with as a health professional are trustworthy” {“La mayoría de las personas con las que trabajo como profesional de la salud son dignas de confianza”}].) For Items 1 and 7, there is a degree of control that the respondent can exercise over the cause (e.g., Item 1, “I feel betrayed by other health professionals in whom I once trusted” [“Me siento traicionado(a) por otros profesionales de la salud en los que alguna vez confié”]) and Item 7, “I have forgiven myself for what happened to me or happened to those I have cared for” [“Me he perdonado por lo que me pasó o sucedió a quienes he cuidado”]). Another explanation for why Item 7 performed poorly in this study, may be an ambiguity in its structure [37]; that is, is the item about forgiving self for what happened to self or for what happened to those the nurse cared for?

Finally, it is worth noting that invariance is a critical aspect in the validation of measurement instruments because it refers to the ability of an instrument to consistently and accurately measure a construct in different populations or groups. The evidence of invariance in this study indicates that the instrument can measure the construct of interest comparably in different age groups. This invariance, however, was determined with a MISS-HP-S that omits three of the scale’s original items. However, we leave to future researchers the need to further evaluate the items that performed less robustly and were removed in this version.

Implications

The results of this study support the validity, internal reliability, and invariance of the MISS-HP-S assessed across different age groups. This suggests that the instrument can be used to measure the construct of moral injury among Spanish-speaking nurses. Thus, the MISS-HP-S can help to effectively assess and monitor moral injury among nurses. The availability of a valid and reliable scale can help identify and address situations and environments that contribute to moral injury, which in turn can improve the mental health and well-being of nurses. Additionally, policymakers can use the results of future studies with the MISS-HP-S to inform and justify the implementation of policies and programs for nurses that address their moral injury.

Additionally, this study contributes to the literature on the validation of measurement instruments for use in healthcare and provides a solid theoretical basis for understanding and improvement the evaluation of such instruments. For example, the invariance of the instrument may also be useful for future theoretical investigations that seek to evaluate and compare the construct of interest in different populations and contexts.

Limitations

For this study, data were collected from a convenience sample of Peruvian nurses. While these nurses were recruited from across the country, it is unknown how representative the data obtained are. Likewise, the sample was limited to nurses and thus fails to provide evidence for the scale’s use among other healthcare professionals in Peru or among nurses in other Spanish-speaking cultures. Because of the recruitment strategies, we were also not able to assess for non-response bias. We do not know if any participant fraudulently or repeatedly entered data. We also did not analyze how responses may have varied by when they responded to the survey.

This study did not consider some important demographic variables, such as gender and background, because the samples in some categories were smaller than 100. Likewise, we did not collect data about level of education given most nurses are similarly trained with only a baccalaureate degree (at best). This limitation could have affected the assessment of scale invariance in relation to these variables. Future research could address this limitation by including larger and more diverse samples, which would allow the assessment of invariance in relation to a wider range of demographic variables.

It must also be remembered that this is an initial, albeit major, step towards evaluating the psychometric properties of the MISS-HP-S. Further testing can provide additional insight about not only various types of validity (e.g., divergent, convergent), but also reliability (test-retest) and responsiveness.

Conclusion

These study findings provide a valuable contribution to the field of research on moral injury in nursing by investigating the psychometric properties of a scale to measure this phenomenon in Peruvian nurses. The findings demonstrate the validity and reliability of the scale, allowing a better understanding of moral injury in this specific group of health professionals and its impact on the quality of care provided to patients. The implications presented for these findings are relevant to professional practice, policy, theory, and research in nursing. Thus, this study provides a solid foundation for future research about nurse moral injury that can inform and potentially impact nursing in the Spanish-speaking diaspora. This work, however, is the initial work towards the MISS-HP-S; further work to determine responsiveness, predictive and other forms of validity, and test-retest reliability, are needed.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Author contributions

MTCC and EJT led the research project. MTCC, JMAI, ROZ, JASV, and MMG wrote the main text of the manuscript. MW, WJMB, and WCMG developed the methodology and performed the statistical analyses, including preparation of graphs and tables. MW translated parts of the manuscript from Spanish into English. EJT reviewed the final English draft and made observations which the other authors addressed. All authors participated in data collection and in online meetings to revise the scale and discuss the results. Additionally, all authors approved the submitted version and agree to be personally accountable for their own contributions.

Funding

This study is part of a larger study titled Spiritual Responses of Nursing Professionals and Associated Factors in the Context of the COVID 19 Pandemic in Peruvian Health Institutions, which was conducted as part of a research collaboration agreement between the institutions of the first author and the last author. This agreement provided limited funding from both institutions to cover some minor expenses, such as the payment of independent translators for the back-translation processes. However, no formal grant funding was used for this study.

Data availability

The translated version of the Moral Injury Symptom Scale for Healthcare Professionals which was the basis of this study is available as a supplementary file for this article. Other data can be made available upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the School of Graduate Studies of the Universidad Peruana Unión (Ref2021-CE-EPG-000075). All participants were given information about the study including possible risks and benefits and were assured of the confidentiality of their responses and the voluntary nature of their participation before being asked to give their informed consent to continue with the questionnaire. Participants were also provided with the name and email address of the lead researcher in case they had any questions or concerns.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

MISS HP-Moral Injury Symptom Scale for Healthcare Professionals

MISS HP-S-Moral Injury Symptom Scale for Healthcare Professionals-Spanish

CA skewness

K kurtosis

IHc corrected homogeneity index

EFA exploratory factor analysis

CFA confirmatory factor analysis

KMO Kaiser-Meyer-Olkin test of sampling adequacy

WLSMV weighted least squares with adjusted variance

χ2 chi-square

RMSEA root mean square error of approximation

SRMR standardized root mean square residuals

CFI Comparative Fit Index

TLI Tucker-Lewis index

MSA Measure of Sampling Adequacy

CMIN/df chi squared over degrees of freedom

Publisher’s Note

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