Feasibility of Rapidly Developing and Widely Disseminating Patient Decision Aids to Respond to Urgent Decisional Needs due to the COVID-19 Pandemic
Bibliographic record
Abstract
To meet urgent decisional needs of retirement/nursing home residents and their families, our interdisciplinary stakeholder team rapidly developed and disseminated patient decision aids (PtDAs) regarding leaving one's residence during the COVID-19 pandemic. The development steps were as follows: identify urgent decisional needs, develop PtDAs using the Ottawa Decision Support Framework template and minimal International PtDA Standards, obtain stakeholder feedback, broadly disseminate, and incorporate user feedback. Within 2 wk, we developed 2 PtDAs for retirement and nursing home living environments that were informed by decisional needs (identified from public responses to related media reports), current pandemic regulations/guidance, and recent systematic reviews. Within 3 wk of their dissemination (websites, international PtDA inventory, Twitter, Facebook, media interviews), the PtDAs were downloaded 10,000 times, and user feedback was positive. Our expert team showed feasible rapid development and wide dissemination of PtDAs to respond to urgent decisional needs. Development efficiencies included access to a well-tested theory-based PtDA template, recent evidence syntheses, and values-based public responses to media reports. Future research includes methods for rapidly collecting user feedback, facilitating implementation, and measuring use and outcomes.
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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 | 0.299 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".