A multistakeholder development process to prioritize and translate COVID-19 health recommendations for patients, caregivers and the public. A case study of the COVID-19 recommendation map
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To develop a digital communication tool to improve the implementation of up-to-date COVID-19 recommendations. Specifically, to improve patient, caregiver and public understanding of healthcare recommendations on prevention, diagnoses and treatment. METHODS: Multi-stakeholder engagement design. In conjunction with the COVID-19 Recommendations and Gateway to Contextualization RecMap, we co-developed a stakeholder prioritization, drafting and editing process to enhance guideline communication and understanding. RESULTS: This paper presents the multi-stakeholder development process with three distinct plain language recommendation formats: formal recommendation, good practice statement, and additional guidance. Our case study of COVID-19 plain language recommendations PLRs addresses both public health interventions (e.g., vaccination, face masks) and clinical interventions (e.g., home pulse oximetry). CONCLUSION: This paper presents a novel approach to engaging stakeholders in improving the communication and understanding of published guidelines during the COVID-19 pandemic.
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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 | 0.086 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".