Managing Resilience and Exhaustion Among Health Care Workers Through Psychological Self-Care: The Impact of Job Autonomy in Interaction With Role Overload
Bibliographic record
Abstract
Francis Maisonneuve,1 Anaïs Galy,1 Patrick Groulx,2 Denis Chênevert,1 Colleen Grady,3 Angela M Coderre-Ball3 1Department of Human Resources Management, HEC Montréal, Montréal, QC, Canada; 2Department of Organisations and Human Resources, ESG UQAM, Montréal, QC, Canada; 3Department of Family Medicine, Queen’s University, Kingston, ON, CanadaCorrespondence: Francis Maisonneuve, Pôle Santé HEC Montréal, 501 De la Gauchetière, Niveau 5, aile D, Montréal, QC, H3T 2A7, Canada, Tel +1 514 998 9183, Email francis.maisonneuve@hec.caPurpose: Drawing on the conservation of resources theory, we explore how job autonomy affects resilience and emotional exhaustion through psychological self-care (PSC). In addition, we study the impact of role overload as a boundary condition which dampens the beneficial effects of job autonomy.Methods: Cross-sectional data was collected through an online survey among Canadian health care workers (HCWs) across multiple organizations. We performed structural equation modeling (SEM) to test the proposed hypotheses (N=860).Results: Job autonomy had a positive relationship with resilience and negative with emotional exhaustion, both through PSC. However, high role overload hinders these relationships.Conclusion: Job autonomy combined with reasonable workload allows HCWs to invest in themselves in the form of PSC, which in turn alleviates their emotional exhaustion and fosters their resilience. Accordingly, this helps HCWs in overcoming both current and future adverse events at work. Valuing autonomy and PSC through communication and contextualized human resource management practices will help support HCWs and health care organizations in turn. Indeed, nurturing resilience and reducing emotional exhaustion will provide and protect the needed individual resources to face future disruptive events, consequently leading to strengthen health care organizations.Keywords: job autonomy, role overload, psychological self-care, resilience, emotional exhaustion
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".