Do people want to be autonomous patients? Preferred roles in treatment decision‐making in several patient populations
Bibliographic record
Abstract
BACKGROUND: What role do people want to play in treatment decision-making (DM)? OBJECTIVE: Examine the role patients indicate they would prefer in making treatment decisions across multiple clinical settings in Ontario, Canada. DESIGN: Secondary analysis of a series of survey/interview-based studies measuring preferred role, conducted in 12 different populations. SETTING AND PARTICIPANTS: Respondents were outpatients, largely but not entirely attending outpatient clinics in large teaching hospitals in urban settings in the Province of Ontario, Canada. The subgroups and sample sizes were: breast cancer (202), prostate disease (202), fractures (202), continence (46), orthopaedic (111), rheumatology (56), multiple sclerosis (22), HIV/AIDS (431), infertility (454), benign prostatic hyperplasia (678) and cardiac disease (300), plus 50 healthy nursing students (for scale validation). MEASUREMENTS: All studies categorized preferred role using the Problem-Solving Decision-Making (PSDM) scale with one or both of the Current Health condition and Chest Pain vignettes. RESULTS: Few respondents preferred an autonomous role (1.2% for the current health condition vignette and 0.7% for the chest pain vignette); most preferred shared DM (77.8% current health condition; 65.1% chest pain) or a passive role (20.3% current health condition; 34.1% chest pain). Familiarity with a clinical condition increases desire for a shared (as opposed to passive) role. Preferences for passive vs. shared roles varied across settings; older and less educated individuals were most likely to prefer passive roles. CONCLUSIONS: Despite consumerist rhetoric among some bioethicists, very few respondents wish an autonomous role. Most wish to share DM with their providers.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".