Decision Coaching to Prepare Patients for Making Health Decisions
Bibliographic record
Abstract
BACKGROUND: Decision coaching is individualized, nondirective facilitation of patient preparation for shared decision making. PURPOSE: To explore characteristics and effectiveness of decision coaching evaluated within trials of patient decision aids (PtDAs) for health decisions. DATA SOURCES: A subanalysis of trials included in the 2011 Cochrane Review of PtDAs. STUDY SELECTION: Eligible trials allowed the effectiveness of decision coaching to be compared with another intervention and/or usual care. DATA EXTRACTION: Two reviewers independently screened 86 trials, extracted data, and appraised quality. DATA SYNTHESIS: Ten trials were eligible. Decision coaching was provided by genetic counselors, nurses, pharmacists, physicians, psychologists, or health educators. Coaching compared with usual care (n = 1 trial) improved knowledge. Coaching plus PtDA compared with usual care (n = 4) improved knowledge and participation in decision making without reported dissatisfaction. Coaching compared with PtDA alone (n = 4) increased values-choice agreement and improved satisfaction with the decision-making process without any difference in knowledge or participation in decision making. Coaching plus PtDA compared with PtDA alone (n = 4) had no difference in knowledge, values-choice agreement, participation in decision making, or satisfaction with the process. Decision coaching plus PtDA was more cost-effective compared with PtDA alone or usual care (n = 1). LIMITATIONS: Methodological quality, number of trials, and description of decision coaching. CONCLUSIONS: Compared with usual care, decision coaching improved knowledge. However, the improvement in knowledge was similar when coaching was compared with PtDA alone. Outcomes for other comparisons are more variable, some trials showing positive effects and other trials reporting no difference. Given the small number of trials and variability in results, further research is required to determine the effectiveness of decision coaching.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".