Translation and validation of the Japanese version of the measure of moral distress for healthcare professionals
Bibliographic record
Abstract
OBJECTIVES: Moral distress occurs when professionals cannot carry out what they believe to be ethically appropriate actions because of constraints or barriers. We aimed to assess the validity and reliability of the Japanese translation of the Measure of Moral Distress for Healthcare Professionals (MMD-HP). METHODS: We translated the questionnaire into Japanese according to the instructions of EORTC Quality of Life group translation manual. All physicians and nurses who were directly involved in patient care at nine departments of four tertiary hospitals in Japan were invited to a survey to assess the construct validity, reliability and factor structure. Construct validity was assessed with the relation to the intention to leave the clinical position, and internal consistency was assessed with Cronbach's alpha. Confirmatory factor analysis was conducted. RESULTS: 308 responses were eligible for the analysis. The mean total score of MMD-HP (range, 0-432) was 98.2 (SD, 59.9). The score was higher in those who have or had the intention to leave their clinical role due to moral distress than in those who do not or did not have the intention of leaving (mean 113.7 [SD, 61.3] vs. 86.1 [56.6], t-test p < 0.001). The confirmatory factor analysis and Cronbach's alpha confirmed the validity (chi-square, 661.9; CMIN/df, 2.14; GFI, 0.86; CFI, 0.88; CFI/TLI, 1.02; RMSEA, 0.061 [90%CI, 0.055-0.067]) and reliability (0.91 [95%CI, 0.89-0.92]) of the instrument. CONCLUSIONS: The translated Japanese version of the MMD-HP is a reliable and valid instrument to assess moral distress among physicians and nurses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".