Restructuring Municipal Government: Labor‐Management Relations and Worker Mental Health
Bibliographic record
Abstract
Cette étude présente une enquête selon la méthode mixte sur l'association qui existe entre les relations travail‐gestion et la santé mentale d'employés d'un secteur municipal subissant une restructuration de style nouvelle gestion publique. L'analyse des données du sondage (N=902) démontre qu'il existe une relation relativement forte et constante entre les pratiques de gestion et la santé psychologique des employés. Des interviews réalisées auprès d'un sous‐échantillon de 54 travailleurs révèlent que le contrôle excessif, l'incompétence et l'indifférence des gestionnaires combinés avec un minimum de récompenses soulignant les efforts consentis par les travailleurs ont pour conséquence que le personnel se sent dévalorisé. Nos résultats démontrent que la santé mentale des travailleurs a étéébranlée, en sapant leur estime de soi et en leur faisant perdre des possibilités d'améliorer leurs conditions de travail. This study is a mixed‐method investigation of the association between labor‐management relations and employees' mental health in a municipal sector undergoing New Public Management‐style restructuring. Analysis of the survey data (N=902) demonstrates a relatively strong and persistent relationship between management practices and employee psychological health. Interviews with a subsample of 54 workers reveal that management's excessive control, incompetence, and unresponsiveness, combined with minimal rewards for workers' efforts, left staff feeling devalued. Our findings suggest that workers' mental health was harmed by the undermining of their sense of self‐worth and the loss of avenues to improve their working conditions.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".