Interprofessional education in academic family medicine teaching units
Bibliographic record
Abstract
PROBLEM ADDRESSED The new family health teams (FHTs) in Ontario were designed to enable interprofessional collaborative practice in primary care; however, many health professionals have not been trained in an interprofessional environment. OBJECTIVE OF PROGRAM To provide health professional learners with an interprofessional practice experience in primary care that models teamwork and collaborative practice skills. PROGRAM DESCRIPTION The 2 academic teaching units of the FHT at McMaster University in Hamilton, Ont, employ 6 types of health professionals and provide learning environments for family medicine residents and students in a variety of health care professions. Learners engage in formal interprofessional education activities and mixed professional and learner clinical consultations. They are immersed in an established interprofessional practice environment, where all team members are valued and contribute collaboratively to patient care and clinic administration. Other contributors to the success of the program include the physical layout of the clinics, the electronic medical record communications system, and support from leadership for the additional clinical time commitment of delivering interprofessional education. CONCLUSION This academic FHT has developed a program of interprofessional education based partly on planned activities and logistic enablers, and largely on immersing learners in a culture of long-standing interprofessional collaboration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".