A Theory-Based Evaluation Framework for Primary Care: Setting the Stage to Evaluate the “Comparison of Models of Primary Health Care in Ontario” Project
Bibliographic record
Abstract
Abstract: Primary care reform has triggered a flood of demonstration projects across Canada that need to be evaluated. This presents a challenge to an evaluator who is uncertain about how to convince clinical investigators to think beyond traditional research designs toward using evaluation approaches. The purpose of this article is to describe the application and benefits of using a theory-based evaluation framework for a large evaluation of four unique models of primary care delivery in Ontario, the Comparison of Models of Primary Health Care in Ontario (COMP-PC) project. Lessons learned are drawn from the authors’ experience in applying the theory-based approach, including the benefits and limitations of having a common framework to facilitate model comparison.
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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 | 0.465 | 0.340 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".