Do choosing wisely tools meet criteria for patient decision aids? A descriptive analysis of patient materials
Bibliographic record
Abstract
OBJECTIVES: Choosing Wisely is a remarkable physician-led campaign to reduce unnecessary or harmful health services. Some of the literature identifies Choosing Wisely as a shared decision-making approach. We evaluated the patient materials developed by Choosing Wisely Canada to determine whether they meet the criteria for shared decision-making tools known as patient decision aids. DESIGN: Descriptive analysis of all Choosing Wisely Canada patient materials. DATA SOURCE: In May 2015, we selected all Choosing Wisely Canada patient materials from its official website. MAIN OUTCOMES AND MEASURES: Four team members independently extracted characteristics of the English materials using the International Patient Decision Aid Standards (IPDAS) modified 16-item minimum criteria for qualifying and certifying patient decision aids. The research team discussed discrepancies between data extractors and reached a consensus. Descriptive analysis was conducted. RESULTS: Of the 24 patient materials assessed, 12 were about treatments, 11 were about screening and 1 was about prevention. The median score for patient materials using IPDAS criteria was 10/16 (range: 8-11) for screening topics and 6/12 (range: 6-9) for prevention and treatment topics. Commonly missed criteria were stating the decision (21/24 did not), providing balanced information on option benefits/harms (24/24 did not), citing evidence (24/24 did not) and updating policy (24/24 did not). Out of 24 patient materials, only 2 met the 6 IPDAS criteria to qualify as patient decision aids, and neither of these 2 met the 6 certifying criteria. CONCLUSIONS: Patient materials developed by Choosing Wisely Canada do not meet the IPDAS minimal qualifying or certifying criteria for patient decision aids. Modifications to the Choosing Wisely Canada patient materials would help to ensure that they qualify as patient decision aids and thus as more effective shared decision-making tools.
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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 | 0.057 | 0.305 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".