Developing Health Care Evaluation Frameworks: Learnings from a Comprehensive Literature Review
Bibliographic record
Abstract
Background: Evidence suggests that shifting from acute, hospital- based health care delivery to preventative, community-based health care (CBHC) makes care more cost-effective, efficient, and equitable. As such, the Government of Alberta, Canada, has recently committed to promoting health and providing health care services to individuals in their own communities. In order to measure their progress, the Alberta’s Ministry of Health approached our research team to create an evidence-based evaluation framework to monitor progress on this initiative. To inform our framework development approach, our team undertook a comprehensive literature review. Methods: We comprehensively reviewed peer-reviewed and grey literature to identify evaluation frameworks and indicators applicable to CBHC programs. Searches were conducted in six databases in June 2018. The search was limited to articles published between 2013 and 2018. The reviewers identified additional studies by scanning reference lists of studies found through the database search, hand-searching grey literature, and obtaining recommendations from expert colleagues in the field. Data were extracted and analyzed by two authors. Results: Ten key themes arose from the article data analysis. Using the themes, team members generated a set of ten overarching recommendations for creating health care evaluation frameworks. Conclusion: This research describes the results of examining the community-based health care evaluation literature and provides ten recommendations for creating new health care evaluation frameworks.
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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 | 0.585 | 0.645 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.076 | 0.044 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.034 | 0.042 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.011 | 0.015 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".