Do people want to be autonomous patients? Preferred roles in treatment decision‐making in several patient populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it