Variables associated with perceived work role performance among mental healthcare professionals: The importance of team dynamics
Bibliographic record
Abstract
Rationale, aims, and objectives: Team effectiveness is associated not only with team design, but also with team dynamics such as work role performance. This study aimed to: (1) identify variables associated with perceived work role performance in a sample of 315 mental health professionals and (2) assess the contributions of team members and team characteristics; organizational and territorial context; team emergent states and team processes.Method: Mental health professionals from 4 health service networks in Quebec, Canada, completed a self-administered questionnaire consisting of standardized scales. Based on a conceptual framework adapted from the Input-Mediator-Output-Input (IMOI) model, independent variables were organized according to: (1) characteristics of team members and their teams, (2) organizational and territorial context, (3) team emergent states and (4) team processes. Their respective contributions to perceived work role performance were tested using a hierarchical regression analysis.Results: Perceived work role performance was associated with younger age (characteristics of team members and their team), familiarity between co-workers (Team emergent states) and belief in interprofessional collaboration, knowledge-sharing, team interdependence and team support (Team processes). Most variation in work role performance was explained by Team emergent states, followed by Team processes.Conclusion: This study tested a large number of variables associated with perceived work role performance in mental healthcare based on a comprehensive and innovative, theory-driven framework. The inclusion of mental health professionals from several types of teams representing mental health networks in different geographical areas added value to the study. The results confirm the need for managers to optimize team emergent states and team processes in order to improve work role performance. Initiatives such as training in teamwork and clinical guidelines are recommended.
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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 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".