Evidence map›Paper›PMID 41079839›Full record

ArticleFrontiers in psychology2025

Psychometric development of a moral distress scale for healthcare education and practice.

Hung-Chang Liao, Ya-Huei Wang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hung-Chang LiaoDepartment of Health Policy and Management, Chung Shan Medical University, Taichung, Taiwan.
Ya-Huei WangDepartment of Applied Foreign Languages, Chung Shan Medical University, Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Unaddressed moral distress may result in psychological, emotional, and physical consequences. The study was to develop and validate a Moral Distress Scale for Healthcare Students and Providers (MDS-HSP) within the Taiwanese healthcare education and clinical contexts, providing a framework for administrators and policymakers to recognize and respond to moral distress in training and practice settings. Methods: Following an extensive literature review and expert discussions, the study performed an exploratory factor analysis (EFA) using SPSS with a sample of 332 participants to determine the hidden structure of the MDS-HSP and evaluate its initial psychometric properties. A subsequent confirmatory factor analysis (CFA) using AMOS with a separate sample of 240 participants was performed to verify the identified factor structure. The testing process included the assessments of validity, reliability, and goodness-of-fit analysis. Results: Following the EFA, the initial 72 items were refined to 42 items across six factors: "acquiescence to patients' rights violations" (8 items), "lack of professional competence" (9 items), "disrespect for patients' autonomy" (10 items), "futile treatment" (5 items), "organizational and social climate" (6 items), and "not in patients' best interest" (4 items). The CFA confirmed the same six scale factors and 42 items. Both EFA and CFA supported the proposed factor structure and demonstrated adequate validity and reliability. Conclusion: The study provided empirical evidence supporting the MDS-HSP as a reliable tool for assessing moral distress experienced by healthcare students and providers. Its use may inform educational strategies, institutional policies, and ethical support mechanisms within healthcare and academic settings.

Indexed as

factor analysishealthcare educationmoral distresspsychometric validationscale development

Identifiers

PMID41079839
PMCPMC12511084

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.