Mental Health in the Workplace: Unions' Role in Identifying and Combating Psychosocial Hazards
Bibliographic record
Abstract
The world of paid work has shifted extraordinarily in the last several decades. Globalization, technology, lean production, the intensification of work, mergers and reorganizations and precarious work have all meant a toughening of the conditions for workers. Unions organize in these conditions, confronting issues of concern to workers. Very little has been written about the role unions play in trying to protect the psychological health of their members. The major question of this thesis is whether unions are identifying and combating psychosocial hazards in the workplace. The thesis adds to knowledge on this subject by analyzing two data sets. First I conduct an analysis of grey literature on the Internet about psychological health and safety concerns. Second, I explore a series of questions with union health and safety experts representing every major Canadian union from each sector of the economy. The questions probe how unions are dealing with psychosocial risks in the workplace, how unions are organizing resistance and building solidarity. My inquiry also explores the issue of how unions deal with return-to-work for workers who have been absent for mental health reasons. I am not a neutral observer: I write from the standpoint of workers. My work has a practical utility to the degree that it can be directly applied to these real life problems facing health and safety practitioners. It attempts to theorize that which these union specialists should do. It also tries to anticipate some practical problems they may need to solve in future as a result of current health and safety practices. I observe real life phenomena and develop theory around them. One of these is that unions resist employer restraints and power and in so doing bump up against managementsâ right to control production and dictate work organization. In this thesis, I show the fledgling ways in which unions are challenging managementsâ typical rights in the interests of better working conditions. I give evidence of three promising practices that unions are adopting and propose that these may be adapted further for initiatives in other sectors. I argue that workersâ psychological health is one potential winner of these strategies. I also propose that union representatives be educated to deal more empathically with members that are absent for reasons of psychological ill health, in advocating for them when they return and by building solidarity among co-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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".