Decision criteria for selecting essential medicines and their connection to guidelines: an interpretive descriptive qualitative interview study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The World Health Organization Model List of Essential Medicines has led to at least 137 national lists. Essential medicines should be grounded in evidence-based guideline recommendations and explicit decision criteria. Essential medicines should be available, accessible, affordable, and the supporting evidence should be accompanied by a rating of the certainty one can place in it. Our objectives were to identify criteria and considerations that should be addressed in moving from a guideline recommendation regarding a medicine to the decision of whether to add, maintain, or remove a medicine from an essential medicines list. We also seek to explore opportunities to improve organizational processes to support evidence-based health decision-making more broadly. METHODS: We conducted a qualitative study with semistructured interviews of key informant stakeholders in the development and use of guidelines and essential medicine lists (EMLs). We used an interpretive descriptive analysis approach and thematic analysis of interview transcripts in NVIVO v12. RESULTS: We interviewed 16 key informants working at national and global levels across all WHO regions. We identified five themes: three descriptive/explanatory themes 1) EMLs and guidelines, the same, but different; 2) EMLs can drive price reductions and improve affordability and access; 3) Time lag and disconnect between guidelines and EMLs; and two prescriptive themes 4) An "evidence pipeline" could improve coordination between guidelines and EMLs; 5) Facilitating the link between the WHO Model List of Essential Medicines (WHO EML) and national EMLs could increase alignment. CONCLUSION: We found significant overlap and opportunities for alignment between guideline and essential medicine decision processes. This finding presents opportunities for guideline and EML developers to enhance strategies for collaboration. Future research should assess and evaluate these strategies in practice to support the shared goal of guidelines and EMLs: improvements in health.
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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 | 0.046 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| 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".