Quality of patient decision aids to support the public making COVID-19 decisions: An online environmental scan
Bibliographic record
Abstract
OBJECTIVE: To identify and appraise the quality of COVID-19 patient decision aids (PtDAs). METHODS: We conducted an environmental scan of online publicly available COVID-19 PtDAs. Two reviewers independently searched and extracted data. We calculated median International Patient Decision Aid Standards (IPDAS) scores and proportion scoring > 70% on Patient Education Materials Information Tool (PEMAT) adequate for understandability and actionability. RESULTS: Of 876 resources identified, 12 were PtDAs. Decisions focused on initial COVID-19 vaccination series (n = 9), location of care for elderly (n = 2), and social distancing (n = 1). All 12 PtDAs were written materials and two had accompanying videos. The median IPDAS score minimizing risk of biased decisions was 4 of 6 items (IQR 1, range 2-4). For PEMAT, 92% had adequate for understandability and none for actionability. CONCLUSIONS: We identified few online publicly available COVID-19 PtDAs and none were about COVID-19 vaccination boosters or treatment. PtDAs scored poorly on actionability and none met all IPDAS criteria for minimizing risk of biased decisions. PRACTICE IMPLICATIONS: PtDA developers for COVID-19 and future pandemics should ensure their PtDAs meet all IPDAS criteria for minimizing risk of bias, have adequate scores for actionability, and are disseminated in the A to Z inventory.
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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 | 0.120 | 0.467 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".