Study of Québec healthworkers shows a positive psychosocial safety climate (PSC) reduces reliance on “workarounds”
Bibliographic record
Abstract
Purpose The purpose was to work out whether by creating a positive working environment reduced turnarounds by reducing the risk of physical fatigue, cognitive weariness and emotional exhaustion. Design/methodology/approach The researchers conducted their study in Québec, with the partnership of the main union of nurses, the Inter-professional Federation of Health of Québec. They received 562 responses. Hypothesis 1 was: “High PSC will decrease workarounds via decreasing physical fatigue as a mediator.” H2 was: “High PSC will decrease workarounds via decreasing cognitive weariness as a mediator.” H3 was: “High PSC will decrease workarounds via decreasing emotional exhaustion as a mediator.” Findings The results supported all the three hypotheses, meaning that physical fatigue, cognitive weariness and emotional exhaustion all mediate relationships between PSC and workarounds. Originality/value The authors argue that their research demonstrates how healthcare organizations would benefit from changing the culture that sees nurses losing an average of 33 minutes on a 7.5-hour shift. The extra pressures lead directly to a workaround culture, the authors say. They argue that organizations should work to ensure that good systems for open communication and mutual trust exist. Managers should encourage workers to talk about difficulties, including issues around blockages and workarounds.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".