Decision aids for people facing health treatment or screening decisions
Bibliographic record
Abstract
BACKGROUND: Decision aids prepare people to participate in preference-sensitive decisions. OBJECTIVES: 1. Create a comprehensive inventory of patient decision aids focused on healthcare options. 2. Review randomized controlled trials (RCT) of decision aids, for people facing healthcare decisions. SEARCH STRATEGY: Studies were identified through databases and contact with researchers active in the field. SELECTION CRITERIA: Two independent reviewers screened abstracts for interventions designed to aid patients' decision making by providing information about treatment or screening options and their associated outcomes. Information about the decision aids was compiled in an inventory; those that had been evaluated in a RCT were reviewed in detail. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data using standardized forms. Results of RCTs were pooled using weighted mean differences (WMD) and relative risks (RR) using a random effects model. MAIN RESULTS: Over 200 decision aids were identified. Of the 131 available decision aids, most are intended for use before counselling. Using the CREDIBLE criteria to evaluate the quality of the decision aids: a) most included potential harms and benefits, credentials of the developers, description of their development process, update policy, and were free of perceived conflict of interest; b) many included reference to relevant literature; c) few included a description of the level of uncertainty regarding the evidence; and d) few were evaluated. Thirty of these decision aids were evaluated in 34 RCTs and another trial evaluated a suite of eight decision aids. An additional 30 trials are yet to be published. Among the trials comparing decision aids to usual care, decision aids performed better in terms of: a) greater knowledge (WMD 19 out of 100, 95% CI: 13 to 24; b) more realistic expectations (RR 1.4, 95%CI: 1.1 to 1.9); c) lower decisional conflict related to feeling informed (WMD -9.1 of 100, 95%CI: -12 to -6); d) increased proportion of people active in decision making (RR 1.4, 95% CI: 1.0 to 2.3); and e) reduced proportion of people who remained undecided post intervention (RR 0.43, 95% CI: 0.3 to 0.7). When simpler were compared to more detailed decision aids, the relative improvement was significant in: a) knowledge (WMD 4 out of 100, 95% CI: 3 to 6); b) more realistic expectations (RR 1.5, 95% CI: 1.3 to 1.7); and c) greater agreement between values and choice. Decision aids appeared to do no better than comparisons in affecting satisfaction with decision making, anxiety, and health outcomes. Decision aids had a variable effect on which healthcare options were selected. REVIEWER'S CONCLUSIONS: The availability of decision aids is expanding with many on the Internet; however few have been evaluated. Trials indicate that decision aids improve knowledge and realistic expectations; enhance active participation in decision making; lower decisional conflict; decrease the proportion of people remaining undecided, and improve agreement between values and choice. The effects on persistence with chosen therapies and cost-effectiveness require further evaluation. Finally, optimal strategies for dissemination need to be explored.
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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 | 0.040 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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".