P208 How Could Who Better Support National And Subnational Governments In Their Efforts To Adapt And Implement Global Recommendations And Decisions? A Systematic Analysis Of Health Systems Guidance And World Health Assembly Resolutions
Bibliographic record
Abstract
Background The World Health Organization’s health systems guidance and the normative standards about health systems endorsed in World Health Assembly (WHA) have the potential to better support national and subnational health systems guidance and policy development processes by including information about the contextual factors that can shape decisions about health systems. Objectives To assess the extent to which WHO health systems guidance and the WHA technical documents include information about how to address a health system problem and how the health system arrangements and political system features can influence decision-making. Methods We reviewed all WHO guidance published since 2008 to 2012 and WHA resolutions published from 2005 to 2012 and included those with a focus on health systems. Two reviewers independently screened and applied the selection criteria to all the documents and extracted the information following pre-established data-extraction forms. Results 13 out of 78 WHO guidance and 14 technical documents out of 207 WHA resolutions had a health system’s focus. Six WHO guidance and 12 WHA documents included information about how to address a health system problem. All WHO guidance included information about delivery arrangements but only three discussed financial arrangements. Two WHO guidance and five WHA documents discussed key features of political systems. Discussions The inclusion of contextual factors, mainly financial arrangements of health systems and political systems features was infrequent among the reviewed documents. Implications for Guideline Developers/Users It is necessary to understand better how to integrate these contextual factors in the process of global guidance development.
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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 | 0.236 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.041 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".