Adapting World Health Organization COVID-19 living guidelines balancing methodological rigor with efficiency and flexibility: a case study from Argentina
Bibliographic record
Abstract
OBJECTIVES: We assessed the perceptions about a new methodological process to translate and adapt the World Health Organization living guidelines for COVID-19 recommendations for therapeutics in Argentina from the guideline development group's (GDG) perspective. METHODS: A tailored adaptation process, linked to a prototype tool and created as part of the GATEWAY project by the MAGIC Evidence Ecosystem Foundation, starts by assessing the recommendation and justification and then examining evidence to decision factors. We focused our evaluation on the adaptation process steps carried out from December 2022 to June 2023. We collected information through (1) observations of the panel meeting, (2) focus group with methods team, (3) semistructured interviews with panel members, (4) postpanel meeting survey, and (5) a satisfaction survey. We carried out descriptive analyses of surveys and content analysis of focus groups and interviews. RESULTS: GDG adapted four recommendations, of which two were modified in direction or strength and elaborated one de novo. The survey showed that most GDG members found the training session (89%) and prepanel meeting survey (100%) facilitated adaptation. Focus groups and interviews showed that GDG agreed that the process considered the relevant local factors to adapt the recommendations and that it was transparent and easy to understand, allowing it to reach a consensus efficiently. GDG valued the process's flexibility and time optimization. They considered the premeeting survey analysis crucial in facilitating the consensus. CONCLUSION: From the GDG perspective, this case study demonstrated that this tailored approach provides a transparent, efficient, and rigorous methodology for translating and adapting the World Health Organization living guidelines for COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".