How can we decrease burnout and safety workaround behaviors in health care organizations? The role of psychosocial safety climate
Bibliographic record
Abstract
Purpose Conducted with a staff of 562 persons working in the health sector in Quebec, mainly nurses, the purpose of this paper is to test the indirect effects of psychosocial safety climate (PSC) on workarounds through physical fatigue, cognitive weariness and emotional exhaustion as mediators. Design/methodology/approach The structural equation method, namely CFA, was used to test the structure of constructs, the reliability and validity of the measurement scales as well as model fit. To test the mediation effects, Hayes’s PROCESS (2013) macro and 95 percent confidence intervals were used and 5,000 bootstrapping re-samples were run. The statistical treatments were carried out with the AMOS software V.24 and SPSS v.22. Findings The results based on bootstrap analysis and Sobel’s test demonstrate that physical fatigue, cognitive weariness and emotional exhaustion mediate the relationship between PSC and safety workarounds. Practical implications The study has important practical implications in detecting blocks and obstacles in the work processes and decreasing the use of workaround behaviors, or in converting their negative consequences into positive contributions. Originality/value To the authors’ knowledge, this is the first study to examine the relationship between PSC, burnout and workaround behaviors. These results could contribute to a better understanding of this construct of workarounds and how to deal with it. Moreover, the test of the concepts of PSC in this study provides support for the theory of “conservation of resources” by proposing an extension of this theory.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".