Team-based care Evaluation and Adoption Model (TEAM) Framework
Bibliographic record
Abstract
OBJECTIVE: To introduce the new Team-based care Evaluation and Adoption Model (TEAM) Framework. QUALITY OF EVIDENCE: The initial TEAM Framework was derived from a series of reviews and consultations with academic and clinical experts. In a parallel process, team-based primary and community care evaluation in Canada was assessed through a structured review of academic literature, followed by a review of policy literature of existing primary care evaluation frameworks. MAIN MESSAGE: The review of academic articles alongside an analysis of policy documents and existing evaluation frameworks in primary care resulted in the development of the 10-dimension TEAM Framework. CONCLUSION: Primary care transformation requires evaluation over time. The TEAM Framework provides a comprehensive framework for assessing evidence needed to support short- and long-term actionable improvements for team-based primary and community care in Canada. This framework will inform the development of an evaluation tool kit for primary care teams.
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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 | 0.254 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".