Team effectiveness in academic primary health care teams
Bibliographic record
Abstract
Primary health care is undergoing significant organizational change, including the development of interdisciplinary health care teams. Understanding how teams function effectively in primary care will assist training programs in teaching effective interprofessional practices. This study aimed to explore the views of members of primary health care teams regarding what constitutes a team, team effectiveness and the factors that affect team effectiveness in primary care. Focus group consultations from six teams in the Department of Family Medicine at Queen's University were recorded and transcribed and qualitative analysis was used to identify themes. Twelve themes were identified that related to the impact of dual goals/obligations of education and clinical/patient practice on team relationships and learners; the challenges of determining team membership including nonattendance of allied health professionals except nurses; and facilitators and barriers to effective team function. This study provides insight into some of the challenges of developing effective primary care teams in an academic department of family medicine. Clear goals and attention to teamwork at all levels of collaboration is needed if effective interprofessional education is to be achieved. Future research should clarify how best to support the changes required for increasingly effective teamwork.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".