Strengthening collaboration for interprofessional primary care teams: Insights and key learnings from six disciplinary perspectives
Bibliographic record
Abstract
We provide a case example of the collaborative process required to plan and implement initiatives to enhance team-based primary care, drawing on experiences of six disciplines working together to create new curricula as part of Team Primary Care. Recommendations to strengthen collaboration from our team include building capacity requires an understanding of unique disciplinary roles and understanding of unique elements of primary care; competencies have to be specifically articulated and demonstrated within a primary care context; interprofessional education within and across disciplines is needed; establishing primary care competencies would provide a common set of skills, knowledge, values, and attitudes to form a foundation in which to build the capacity of the interprofessional primary care workforce; and interprofessional collaboration is needed in implementing team-based primary care in practice and in preparing an interprofessional workforce prepared to leverage the expertise of the entire team.
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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 | 0.047 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.003 | 0.041 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".