BETTER GUIDANCE FOR BETTER HEALTH SYSTEMS
Bibliographic record
Abstract
Health systems guidance (HSG) can be defined as a set of options presented to policy-makers on how to address a particular health systems issue/challenge (Bosch-Capblanch et al, 2012). However, best strategies for developing HSG and translating it into policy are poorly understood at this time (Lavis et al, 2012). Additionally, there is currently no instrument that has the capacity to discriminate between higher quality HSG from those of lower quality. This thesis begins to address these gaps through three original scientific contributions that use a range of methodological approaches to design a tool that will be used to direct the development, appraisal and reporting of HSG. Taken together, the chapters present three stages conducted in a sequence: Stage 1: A critical interpretive synthesis of the literature to generate a draft list of candidate concepts (items, criteria or domains) for the HSG tool, with their descriptions and a specific set of operational considerations to optimize their use. Stage 2: Results from a survey conducted across the six World Health Organization (WHO) health regions to evaluate the importance of the candidate concepts, assess the appropriateness of their descriptions, and identify any missing components, in order to generate a beta version of the HSG tool. Stage 3: Results from a survey conducted across the six WHO health regions to test the usability of the beta version of the HSG tool to determine its feasibility of application and ease of understanding, in order to generate version 1 of the HSG tool. As a whole, the chapters presented in this thesis provide substantive, methodological and disciplinary contributions to the field of health systems research and in particular, about how to support the production, evaluation and reporting of high quality HSG for the purposes of strengthening health systems in low, middle and high income countries. The core deliverable of this program of research is version one of the HSG tool, the AGREE-HS (Appraisal of Guidelines Research and Evaluation – Health Systems).
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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 | 0.069 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".