Profiling mental health professionals in relation to perceived interprofessional collaboration on teams
Bibliographic record
Abstract
OBJECTIVES: This study aims at identifying profiles of mental health professionals based on individual, interactional, structural and professional role characteristics related to interprofessional collaboration. METHODS: = 315) working in primary health care and specialized mental health teams in four Quebec local service networks completed a self-administered questionnaire eliciting information on individual, interactional, structural and professional role characteristics. RESULTS: Cluster analysis identified four profiles of mental health professionals. Those with the highest interprofessional collaboration scores comprised two profiles labeled "highly collaborative female professionals with fewer conflicts and more knowledge sharing and integration" and "highly collaborative male professionals with fewer conflicts, more participation in decision-making and mutual trust." By contrast, the profile labeled "slightly collaborative professionals with high seniority, many conflicts and less knowledge integration and mutual trust" had the lowest interprofessional collaboration score. Another profile positioned between these groups was identified as "moderately collaborative female psychosocial professionals with less participation in decision-making." DISCUSSION AND CONCLUSION: Organizational support, participation in decision-making, knowledge sharing, knowledge integration, mutual trust, affective commitment toward the team, professional diversity and belief in the benefits of interdisciplinary collaboration were features associated with profiles where perceived interprofessional collaboration was higher. These team qualities should be strongly encouraged by mental health managers for improving interprofessional collaboration. Training is also needed to promote improvement in interprofessional collaboration competencies.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".