A Results-Based Logic Model for Primary Healthcare: A Conceptual Foundation for Population-Based Information Systems
Bibliographic record
Abstract
A conceptual framework for population-based information systems is needed if these data are to be created and used to generate information to support healthcare policy, management and practice communities that seek to improve quality and account for progress in primary healthcare (PHC) renewal. This paper describes work conducted in British Columbia since 2003 to (1) create a Results-Based Logic Model for PHC using the approach of the Treasury Board of Canada in designing management and accountability frameworks, together with a literature review, policy analysis and broad consultation with approximately 650 people, (2) identify priorities for information within that logic model, (3) use the logic model and priorities within it to implement performance measurement and research and (4) identify how information systems need to be structured to assess the impact of variation or change in PHC inputs, activities and outputs on patient, population and healthcare system outcomes. The resulting logic model distinguishes among outcomes for which the PHC sector should be held more or less accountable.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".