Social Workers' Collective Strategies to Mitigate the Impact of Organizational Constraints on Occupational Wellbeing
Bibliographic record
Abstract
Summary In Québec, social workers face numerous psychosocial risk factors affecting their psychological health, owing to staff shortages, work overload, and lack of recognition within the context of ongoing organizational restructuring. This study uses a systemic framework of individual and collective strategies to analyze those that enable social workers to manage the psychosocial risk factors they face and stay at work. In a qualitative exploratory study conducted between 2020 and 2022, 11 semistructured interviews were conducted with social workers (8 women and 3 men) from different intervention sectors in Québec. Results The results reveal that social workers feel unrecognized in interdisciplinary teams. They are overburdened by the clerical nature of the tasks imposed, which distort their profession and affect their clinical judgment. Individual levers and strategies favored include love of the job and adaptability, self-control, prioritizing emergencies, and working overtime. Collective strategies, involving colleagues, seem to encourage social workers to stay at work. Yet the organization of work, based on a standardized vision of social practice, threatens this work collective. Applications It is important to consider the work collective as a resource that can promote wellbeing at work and retention of social workers. Establishing conditions at interpersonal and organizational levels would help prevent obstacles to the deployment of strategies that mobilize the collective. Ultimately, a systemic approach to social workers’ health protection strategies would ensure the sustainability of interventions aimed at promoting the psychological health of social workers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".