The Effects of the Civility, Respect, and Engagement in the Workplace (CREW) Program on Social Climate and Work Engagement in a Psychiatric Ward in Japan: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Good social climate and high work engagement are important factors affecting outcomes in healthcare settings. This study observed the effects of a program called Civility, Respect, and Engagement in the Workplace (CREW) on social climate and staff work engagement in a psychiatric ward of a Japanese hospital. METHODS: The program comprised 18 sessions installed over six months, with each session lasting 30-min. Participation in the program was recommended to all staff members at the ward, including nurses, medical doctors, and others, but it was not mandatory. A serial cross-sectional study collected data at four time-points. Nurses (n = 17 to 22), medical doctors (n = 9 to 13), and others (n = 6 to 10) participated in each survey. The analysis of variance was used to evaluate the changes in the following dependent variables, the Essen climate evaluation schema (EssenCES), the CREW civility scale, and the Utrecht work engagement scale (UWES) over time. RESULT: We found no significant effects. The effect size (Cohen's d) for EssenCES was 0.35 from baseline to post-installation for all staff members. Effect sizes for EssenCES for medical doctors and UWES for nurses were 0.79 and 0.56, respectively, from baseline to post-program. CONCLUSIONS: Differences in social climate and work engagement among Japanese healthcare workers between the baseline and post-installation of the CREW program were non-significant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".