User-experience testing of an evidence-to-decision framework for selecting essential medicines
Bibliographic record
Abstract
Essential medicine lists (EMLs) are important medicine prioritization tools used by the World Health Organization (WHO) EML and over 130 countries. The criteria used by WHO's Expert Committee on the Selection and Use of Essential Medicines has parallels to the GRADE Evidence-to-Decision (EtD) frameworks. In this study, we explored the EtD frameworks and a visual abstract as adjunctive tools to strengthen the integrate evidence and improve the transparency of decisions of EML applications. We conducted user-experience testing interviews of key EML stakeholders using Morville's honeycomb model. Interviews explored multifaceted dimensions (e.g., usability) on two EML applications for the 2021 WHO EML-long-acting insulin analogues for diabetes and immune checkpoint inhibitors for lung cancer. Using a pre-determined coding framework and thematic analysis we iteratively improved both the EtD framework and the visual abstract. We coded the transcripts of 17 interviews with 13 respondents in 103 locations of the interview texts across all dimensions of the user-experience honeycomb. Respondents felt the EtD framework and visual abstract presented complementary useful and findable adjuncts to the traditional EML application. They felt this would increase transparency and efficiency in evidence assessed by EML committees. As EtD frameworks are also used in health practice guidelines, including those by the WHO, respondents articulated that the adoption of the EtD by EML applications represents a tangible mechanism to align EMLs and guidelines, decrease duplication of work and improve coordination. Improvements were made to clarify instructions for the EtD and visual abstract, and to refine the design and content included. 'Availability' was added as an additional criterion for EML applications to highlight this criterion in alignment with WHO EML criteria. EtD frameworks and visual abstracts present additional important tools to communicate evidence and support decision-criteria in EML applications, which have global health impact. Access to essential medicines is important for achieving universal health coverage, and the development of essential medicine lists should be as evidence-based and trustworthy as possible.
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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 | 0.195 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".