Values and preferences in COVID-19 public health guidelines: A systematic review
Bibliographic record
Abstract
Abstract Background Internationally accepted standards for trustworthy guidelines include the necessity to ground recommendations in values and preferences. Considering values and preferences respects the rights of citizens to participate in health decision-making and ensures that guidelines align with the needs and priorities of the communities they are intended to serve. Early anecdotal reports suggest that COVID-19 public health guidelines did not consider values and preferences. Objective To capture and characterize whether and how COVID-19 public health guidelines considered values and preferences. Methods We performed a systematic review of COVID-19 public health guidelines. We searched the eCOVID19 RecMap platform—a comprehensive international catalog of COVID-19 guidelines—up to July 2023. We included guidelines that made recommendations addressing vaccination, masking, isolation, lockdowns, travel restrictions, contact tracing, infection surveillance, and school closures. Reviewers worked independently and in duplicate to review guidelines for consideration of values and preferences. Results Our search yielded 129 eligible guidelines, of which 43 (33.3%) were published by national organizations, 73 (56.6%) by international organizations, and 14 (10.9%) by professional societies and associations. Twenty-six (20.2%) guidelines considered values and preferences. Among guidelines that considered values and preferences, most did so to assess the acceptability of recommendations (23; 88.5%) and by referencing published research (24; 92.3%). Guidelines only occasionally engaged laypersons as part of the guideline development group (6; 23.1%). None of the guidelines performed systematic reviews of the literature addressing values and preferences. Conclusion Most COVID-19 public health guidelines did not consider values and preferences. When values and preferences were considered, it was suboptimal. Disregard for values and preferences in guidelines might have partly contributed to divisive and unpopular COVID-19 policies. Given the possibility of future health emergencies, we recommend guideline developers identify efficient methods for considering values and preferences in crisis situations.
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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 | 0.082 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".