Developing and refining the methods for a ‘one-stop shop’ for research evidence about health systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Policymakers, stakeholders and researchers have not been able to find research evidence about health systems using an easily understood taxonomy of topics, know when they have conducted a comprehensive search of the many types of research evidence relevant to them, or rapidly identify decision-relevant information in their search results. METHODS: To address these gaps, we developed an approach to building a 'one-stop shop' for research evidence about health systems. We developed a taxonomy of health system topics and iteratively refined it by drawing on existing categorization schemes and by using it to categorize progressively larger bundles of research evidence. We identified systematic reviews, systematic review protocols, and review-derived products through searches of Medline, hand searches of several databases indexing systematic reviews, hand searches of journals, and continuous scanning of listservs and websites. We developed an approach to providing 'added value' to existing content (e.g., coding systematic reviews according to the countries in which included studies were conducted) and to expanding the types of evidence eligible for inclusion (e.g., economic evaluations and health system descriptions). Lastly, we developed an approach to continuously updating the online one-stop shop in seven supported languages. RESULTS: The taxonomy is organized by governance, financial, and delivery arrangements and by implementation strategies. The 'one-stop shop', called Health Systems Evidence, contains a comprehensive inventory of evidence briefs, overviews of systematic reviews, systematic reviews, systematic review protocols, registered systematic review titles, economic evaluations and costing studies, health reform descriptions and health system descriptions, and many types of added-value coding. It is continuously updated and new content is regularly translated into Arabic, Chinese, English, French, Portuguese, Russian, and Spanish. CONCLUSIONS: Policymakers and stakeholders can now easily access and use a wide variety of types of research evidence about health systems to inform decision-making and advocacy. Researchers and research funding agencies can use Health Systems Evidence to identify gaps in the current stock of research evidence and domains that could benefit from primary research, systematic reviews, and review overviews.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchScholarly communication Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.326 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it