One down, fifty to go: managers’ perceptions of their workload and how they cope with it to maintain their psychological health
Bibliographic record
Abstract
Background: Like many other countries, healthcare services in Canada face numerous organizational changes with the main objective of doing more with less. The approach taken within different healthcare networks has brought about a reform in healthcare facilities in Quebec, leading to several mergers and eliminating over 1,000 managerial positions. As a result, this has placed a progressively heavier workload on the shoulders of the remaining managers. Research on mental health in the workplace has mainly focused with the workforce and generally neglects managers. However, studies have shown that workload is a risk factor for managers. Therefore, the objectives of our study are to (1) better understand the elements that make up a manager's workload and the factors that influence it and (2) identify the coping strategies used by managers to deal with their workloads. Methods: Employing a qualitative approach, we analyzed 61 semistructured interviews through an abductive method, utilizing diverse frameworks for data analysis. The participants came from the same Quebec healthcare establishment. Results: Our findings align with the notion that workload is a multifaceted phenomenon that warrants a holistic analysis. The workload mapping framework we propose for healthcare network managers enables pinpointing those factors that contribute to the burden of their workload. Ultimately, this workload can detrimentally impact the psychological wellbeing of employees. Conclusion: In conclusion, this study takes a comprehensive look at workload by using a holistic approach, enabling a more comprehensive understanding of this phenomenon. It also allows for the identification of coping strategies used by managers to deal with their workloads. Finally, our results can provide valuable guidance for the interventions aimed at addressing workload issues among healthcare network managers in Quebec by utilizing the specific elements we have identified.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".