Family health teams: can health professionals learn to work together?
Bibliographic record
Abstract
OBJECTIVE: To learn what educators across the health professions involved in primary health care think about the use and development of academic family health teams to provide, teach, and model interprofessional collaboration and about the introduction of interprofessional education (IPE) within structured academic primary care. DESIGN: Qualitative study using focus groups. SETTING: Higher education institutions across Ontario. PARTICIPANTS: Purposeful sample of 36 participants from nursing, pharmacy, speech language pathology, occupational and physical therapy, social work, and family medicine. METHOD: Participants were invited to join focus groups of 6 to 8 health professionals. Themes were derived from qualitative analysis of data gathered using a grounded-theory approach. MAIN FINDINGS: Three major themes were identified: the lack of consensus on opportunities for future academic family health teams to teach IPE, the lack of formalized teaching of interprofessional collaboration and the fact that what little has been developed is primarily for family physicians and hardly at all for other health professionals, and the confusion around the definition of IPE across health professions. CONCLUSION: The future role of family health teams in academic primary care settings as a place for learners to see teamwork in action and to learn collaboration needs to be examined. Unless academic settings are developed to provide the necessary training for primary health care professionals to work in teams, a new generation of health care professionals will continue to work in status quo environments, and reform initiatives are unlikely to become sustainable over time.
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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 | 0.008 | 0.030 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".