The Impact of Explicit Values Clarification Exercises in a Patient Decision Aid Emerges After the Decision Is Actually Made
Bibliographic record
Abstract
PURPOSE: To determine if particular values clarification exercises included in a patient decision aid had discernible impact on postdecisional regret in patients with early-stage prostate cancer. METHODS: A multicenter randomized controlled trial compared 2 versions of a computerized patient decision aid: only structured information compared to the structured information plus values clarification exercises. Assessments were conducted during the decision aid visit; telephone follow-up interviews were conducted when patients made their decisions with their physician, 3 months after completing treatment, and >1 year later (per a mailing). Outcome measures included the Decisional Conflict Scale, the Preparation for Decision Making Scale, and the Decision Regret Scale. RESULTS: A total of 156 patients participated, 75 provided information only and 81 provided information plus values clarification exercises. The groups did not differ significantly on any outcome evaluated at the decision aid visit; in both groups, decisional conflict decreased immediately after using the decision aid. Between-group differences emerged after the decision was actually made. The values clarification exercises group reported higher Preparation for Decision Making Scale scores at the decision follow-up and at the >1-year follow-up. Regret did not differ significantly between groups at the 3-month follow-up but was lower for the values clarification exercises group than for the information group at the >1-year follow-up. CONCLUSION: The results suggest that the values clarification exercises led to better preparation for decision making and to less regret. The impact, however, only emerged after the decision was made.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".