Adapting the Healthy Work Environment Assessment Tool for French-Canadian Intensive Care Nurses
Bibliographic record
Abstract
BACKGROUND: Self-administered instruments are used to measure components of work environments that cannot be measured directly. The Healthy Work Environment Assessment Tool (HWEAT) of the American Association of Critical-Care Nurses is a promising instrument. However, it is available only in English and Japanese, precluding its use in other populations and cross-national comparisons. OBJECTIVES: To describe the Canadian French translation and cross-cultural adaptation of the HWEAT (F-HWEAT) and to explore its factor structure and psychometric properties. METHODS: Cross-cultural adaptation of the HWEAT and collection of evidence of validity via an electronic cross-sectional survey. RESULTS: A total of 564 intensive care unit nurses participated in the validation study. Confirmatory factor analysis supported the presence of a single overarching factor measured by the F-HWEAT. The Cronbach α for the instrument was 0.89 (95% CI, 0.88-0.91). The mean and median interitem correlations were both 0.32, and item-partial total correlations ranged from 0.33 to 0.64. The overall F-HWEAT score indicated that nurses believed their work environment needed improvements. Moderate positive correlations were found between the overall F-HWEAT score and nurses' perceptions of care quality (r = 0.45 [95% CI, 0.38-0.51]) and safety (r = 0.48 [95% CI, 0.40-0.55]). CONCLUSION: The results support the use of the F-HWEAT in French-speaking populations. Using the F-HWEAT will help elucidate areas needing improvement and expand global dialogues about healthy critical care work environments. With this information, nurse leaders and researchers can develop and implement modern strategies to improve the work conditions of intensive care unit 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 0.000 |
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".