Guidance and/or Decision Coaching with Patient Decision Aids: Scoping Reviews to Inform the International Patient Decision Aid Standards (IPDAS)
Bibliographic record
Abstract
INTRODUCTION: In 2005, the International Patient Decision Aid Standards (IPDAS) collaboration identified guidance and decision coaching as important dimensions of patient decision aids (PtDAs) and developed a set of quality criteria. We sought to update definitions, theoretical rationale, and evidence for guidance and/or decision coaching used within or alongside PtDAs for the IPDAS update 2.0. METHODS: We conducted 2 scoping reviews on guidance and decision coaching, including systematic searches and a hand search of the Cochrane Review on PtDAs. Eligible studies were randomized controlled trials (RCTs) on guidance or decision coaching used with/alongside PtDAs. Data, including conceptual models, were summarized narratively and with meta-analyses when appropriate. RESULTS: Of 1022 citations, we found no RCTs that evaluated guidance in PtDAs. The 2013 definition for guidance was endorsed, and we made minimal changes to the description of guidance. Of 3039 citations, we identified 21 RCTs on decision coaching informed by 5 conceptual models stating that people exposed to decision coaching are more likely to progress in making informed decisions consistent with their values. Compared to usual care, decision coaching with PtDAs led to improved knowledge mean difference [MD], 19.5/100; 95% confidence interval [CI], 10.0-29.0; 5 RCTs). Compared to decision coaching alone, PtDAs led to a small improvement in knowledge (MD, 3.6/100; 95% CI, 1.0-6.3; 3 RCTs). There were variable effects on other outcomes. We simplified the decision coaching definition slightly and defined minimal decision coaching elements. CONCLUSION: We found no evidence on which to propose changes in guidance in IPDAS. Decision coaching is continuing to be used alongside PtDAs, but there is inadequate evidence on the added effectiveness compared to PtDAs alone. The decision coaching definition was updated with minimal elements.
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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 | 0.200 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".