Health systems guidance appraisal—a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Health systems guidance (HSG) are systematically developed statements that assist with decisions about options for addressing health systems challenges, including related changes in health systems arrangements. However, the development, appraisal, and reporting of HSG poses unique conceptual and methodological challenges related to the varied types of evidence that are relevant, the complexity of health systems, and the pre-eminence of contextual factors. To address this gap, we are conducting a program of research that aims to create a tool to support the appraisal of HSG and further enhance HSG development and reporting. The focus of this paper was to conduct a knowledge synthesis of the published and grey literatures to determine quality criteria (concepts) relevant for this process. METHODS: We applied a critical interpretive synthesis (CIS) approach to knowledge synthesis that enabled an iterative, flexible, and dynamic analysis of diverse bodies of literature in order to generate a candidate list of concepts that will constitute the foundational components of the HSG tool. Using our review questions as compasses, we were able to guide the search strategy to look for papers based on their potential relevance to HSG appraisal, development, and reporting. The search strategy included various electronic databases and sources, subject-specific journals, conference abstracts, research reports, book chapters, unpublished data, dissertations, and policy documents. Screening the papers and data extraction was completed independently and in duplicate, and a narrative approach to data synthesis was executed. RESULTS: We identified 43 papers that met eligibility criteria. No existing review was found on this topic, and no HSG appraisal tool was identified. Over one third of the authors implicitly or explicitly identified the need for a high-quality tool aimed to systematically evaluate HSG and contribute to its development/reporting. We identified 30 concepts that may be relevant to the appraisal of HSG and were able to cluster them into three meaningful domains: process principles, content, and context principles. CONCLUSIONS: Our study showed the role that the quality criteria play in the development, appraisal, and reporting of HSG and demonstrated the link and resonance within and between the various concepts in the three domains.
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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 | 0.510 | 0.728 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.062 | 0.041 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".