Variables associated with interprofessional collaboration: a comparison between primary healthcare and specialized mental health teams
Bibliographic record
Abstract
BACKGROUND: This study has two aims: first, to identify variables associated with interprofessional collaboration (IPC) among a total of 315 Quebec mental health (MH) professionals working in MH primary care teams (PCTs, N = 101) or in specialized service teams (SSTs, N = 214); and second, to compare IPC associated variables in MH-PCTs vs MH-SSTs. METHODS: A large number of variables acknowledged as strongly related to IPC in the literature were tested. Multivariate regression models were performed on MH-PCTs and MH-SSTs respectively. RESULTS: Results showed that knowledge integration, team climate and multifocal identification were independently and positively associated with IPC in both MH-PCTs and MH-SSTs. By contrast, knowledge sharing was positively associated with IPC in MH-PCTs only, and organizational support positively associated with IPC in MH-SSTs. Finally, one variable (age) was significantly and negatively associated with IPC in SSTs. CONCLUSIONS: Improving IPC and making MH teams more successful require the development and implementation of differentiated professional skills in MH-PCTs and MH-SSTs by care managers depending upon the level of care required (primary or specialized). Training is also needed for the promotion of interdisciplinary values and improvement of interprofessional knowledge regarding IPC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".