A Systematic Literature Review Comparing Primary and Community Health Care Indicators and Measurement Frameworks
Bibliographic record
Abstract
Measurement frameworks are essential in primary and community healthcare to help reduce unsustainable healthcare costs in many jurisdictions including Ontario, Canada. This paper presents a literature review of studies measuring the success of primary and community healthcare initiatives around the world carried out after 2003 in more than 15 countries. Some initiatives were fully deployed and others were in research or pilot mode. A comprehensive set of indicators is identified spanning four categories and nine domain areas. We discuss our observations showing the discrepancies that exist amongst the various studies and analyze the problems associated with these gaps. We proposed a new approach that we intend to pursue in more detail in future work. There is a lack of maturity in measuring the success of primary and community healthcare initiatives. There are opportunities in improving the situation by defining aggregate indices, working on standardization of indicators, and identifying measures that contribute to improving the system in place based on mining existing data and using a heuristics-based approach.
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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 | 0.030 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.049 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".