Decision aids: are they worth it? A systematic review
Bibliographic record
Abstract
OBJECTIVES: The field of consumer decision support interventions has grown rapidly in the last 20 years, particularly since the increased formalization of evidence-based decision-making in health care. However, little is known as yet about the effect these decision aids have on relevant outcomes. To identify outcomes influenced by consumer decision aids (CDAs) and the particular effects of CDAs on those outcomes. METHOD: Published reports of randomized controlled trials and other designs in English identified from searches of Medline, Cancerlit, CINAHL, PsycINFO, Social Science Abstracts, the Cochrane Library and Current Contents, and from ancestry searches, hand searches and consultation with key informants. Studies were included if consumers were the decision-makers, decisions were real and not hypothetical, the intervention met a strict definition of a CDA and decisions involved treatment or screening. Data on setting, subjects, decision types, decision aid features and outcomes were extracted. The validity of each study included was evaluated by two team members. Summative ratings were calculated and categorized as high, medium and low. RESULTS: Over 500 titles were initially assessed; 96 study reports were obtained and screened for inclusion. Twenty-two reports representing 20 studies met the inclusion criteria. Eight studies with a low rating were excluded. Results were synthesized from the remaining 12 studies. Published studies of CDAs are increasing although still few in number. Outcomes such as knowledge and decision-making processes are influenced by CDAs but effects on treatment preferences, the actual decision or other outcomes have yet to be clearly established. CONCLUSIONS: Improved outcomes result from the use of CDA in some categories of outcome but insufficient evidence exists yet to support improved outcomes in all categories or to evaluate whether, given the considerable costs of many CDAs, funding for extensive ongoing development and testing can be sustained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".