Health promoter, advocate, legitimiser — the many roles of WHO guidelines: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Properly implemented evidence-based clinical and public health guidelines can improve patient outcomes. WHO has been a major contributor to guideline development, publishing more than 250 guidelines on various topics since 2008. However, well-developed guidelines can only be effective if they are adequately and appropriately implemented. Herein, we aimed to explore whether and how WHO guidelines are implemented in local contexts to inform the success of future guideline implementation. METHODS: Seventeen interviews were carried out between March 2018 and December 2018 with WHO guideline developers, headquarter staff, and regional and country office staff. Participants were purposely sampled from a variety of WHO guidelines and snowball sampling was used to identify regional and country office staff. The deidentified transcripts were analysed through three phases of coding, using grounded theory as the analytic approach. RESULTS: WHO guidelines played a variety of roles in the work of WHO at all levels. WHO officers and local government officials used WHO guidelines to influence health policy. We categorised the uses of guidelines as (1) directly changing policy, (2) justifying policy change, (3) engaging stakeholders, (4) being guarantors of legitimacy, (5) being advocacy tools, and (6) intertwining with WHO's various roles. Participants refuted the perception of the guidelines as mere lists of technical recommendations that needed to be implemented in different contexts. We found that the existence, quality and credibility, rather than the content of the guidelines, are the keys to health policy change initiatives in different local contexts. CONCLUSIONS: Used as a guarantor of legitimacy by policy-makers, WHO guidelines can be better positioned to influence health policy and practice change. Understanding the various roles of guidelines can help WHO developers package guidelines to optimise their effective implementation. ETHICS: This project was conducted with ethics approval from The University of Sydney (Project number: 2017/723) and WHO (Protocol ID: 00001).
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".