Improving WHO’s Understanding of WHO Guideline Uptake and Use in Member States: A Scoping Review
Bibliographic record
Abstract
Abstract Background: World Health Organization (WHO) publishes public health, clinical and data-use guidelines to guide member states to achieve better health outcomes. Furthermore, the WHO's Thirteenth General Programme of Work for 2019−2023 prioritizes strengthening its normative functional role and uptake of normative and standard-setting products, including guidelines at the country level. Therefore, understanding WHO guideline uptake by the member states, particularly the low and middle-income countries (LMICs), is of utmost importance for the organization and scholarship.Methods:We conducted a scoping review using systematic review methods. We used a comprehensive search strategy to include published literature in English between 2007 - 2020. Six databases - CINAHL, Cochrane Library, PubMed, Embase, SCOPUS, Google Scholar) and grey literature were searched. The review adhered to the PRISMA guidelines for reporting the searches, screening and identification of evaluation studies from the literature. A narrative synthesis of the evidence around key barriers and challenges for WHO guideline uptake in LMICs is thematically presented.Results: The scoping review included 48 studies, and the findings were categorised into four themes –1) Lack of national legislation, regulations and policy coherence, 2) Inadequate experience, expertise and training of healthcare providers for guidelines uptake, 3) Funding limitations for guideline uptake and use, and 4) Inadequate healthcare infrastructure for guidelines compliance. These challenges were situated in the member states’ health systems. The findings suggest governance was often weak within the existing health systems amongst most of the LIMCs studied, as was the guidance provided by WHO’s guidelines on governance requirements. This challenge was further exacerbated by a lack of accountability and transparency mechanisms for uptake of guidelines and implementation. In addition, the WHO guidelines themselves were either unclear or weak and were technically challenging for some health conditions; but primarily, WHO guidelines were used as a reference by member states when they developed their national guidelines. Conclusions: The challenges identified reflect the national health systems’ (in)ability to allocate, implement, and monitor the guidelines. Historically this is beyond the remit of WHO but member states could benefit from WHO implementation guidance on requirements and needs for successful uptake and use of WHO guidelines.
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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 | 0.134 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".