Development of rapid guidelines: 2. A qualitative study with WHO guideline developers
Bibliographic record
Abstract
BACKGROUND: Situations such as public health emergencies and outbreaks necessitate the development and publication of high-quality recommendations within a condensed timeframe. For example, WHO has produced examples of and guidance for the development of rapid guidelines (RGs). However, more information is needed to understand the experiences and perceptions of guideline developers. This is the second of a series of three articles addressing methodological issues around RGs. This study describes the perceptions and experiences of guideline developers at WHO about RGs. METHODS: We conducted interviews consisting of open- and closed-ended questions with guideline developers at WHO. Our analysis described the definition and rationale of RGs, the differences from regular guidelines with regard to timelines from topic definition until publication, barriers to identifying the evidence and the lack of a standard methodology to develop RGs. RESULTS: We interviewed 10 participants, the majority of whom were comfortable with the current WHO definition of RGs. Most stated that the rationale for developing RGs should be in response to new evidence about efficacy, cost-effectiveness or safety. Respondents differed with regards to the amount of time RGs should take. While the majority of participants agreed that guidelines should be based on a systematic review, this step in the process was considered the most time and resource intensive. Challenges for developing RGs included limited personnel and financial resources as well as the lack of evidence. Facilitators, in turn, that may improve RG development include additional financial and personnel resources as well as the use of virtual meetings. CONCLUSIONS: While our study suggests a strong need and rationale for the development of RGs, standardisation of timelines and guidance on panel composition, peer-review process, conduct of meetings and sources of permissible evidence require further research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".