Job satisfaction among mental healthcare professionals: The respective contributions of professional characteristics, team attributes, team processes, and team emergent states
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the respective contribution of professional characteristics, team attributes, team processes, and team emergent states on the job satisfaction of 315 mental health professionals from Quebec (Canada). METHODS: Job satisfaction was measured with the Job Satisfaction Survey. Independent variables were organized into four categories according to a conceptual framework inspired from the Input-Mediator-Outcomes-Input Model. The contribution of each category of variables was assessed using hierarchical regression analysis. RESULTS: Variations in job satisfaction were mostly explained by team processes, with minimal contribution from the other three categories. Among the six variables significantly associated with job satisfaction in the final model, four were team processes: stronger team support, less team conflict, deeper involvement in the decision-making process, and more team collaboration. Job satisfaction was also associated with nursing and, marginally, male gender (professional characteristics) as well as with a stronger affective commitment toward the team (team emergent states). DISCUSSION AND CONCLUSION: Results confirm the importance for health managers of offering adequate support to mental health professionals, and creating an environment favorable to collaboration and decision-sharing, and likely to reduce conflicts between team members.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".