What can organizations do to improve family physicians' interprofessional collaboration? Results of a survey of primary care in Quebec.
Bibliographic record
Abstract
OBJECTIVE: To assess the degree of collaboration in primary health care organizations between FPs and other health care professionals; and to identify organizational factors associated with such collaboration. DESIGN: Cross-sectional survey. SETTING: Primary health care organizations in the Montreal and Monteregie regions of Quebec. PARTICIPANTS: Physicians or administrative managers from 376 organizations. MAIN OUTCOME MEASURES: Degree of collaboration between FPs and other specialists and between FPs and nonphysician health professionals. RESULTS: Almost half (47.1%) of organizations reported a high degree of collaboration between FPs and other specialists, but a high degree of collaboration was considerably less common between FPs and nonphysician professionals (16.5%). Clinic collaboration with a hospital and having more patients with at least 1 chronic disease were associated with higher FP collaboration with other specialists. The proportion of patients with at least 1 chronic disease was the only factor associated with collaboration between FPs and nonphysician professionals. CONCLUSION: There is room for improvement regarding interprofessional collaboration in primary health care, especially between FPs and nonphysician professionals. Organizations that manage patients with more chronic diseases collaborate more with both non-FP specialists and nonphysician professionals.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".