How does trust affect patient preferences for participation in decision‐making?
Bibliographic record
Abstract
OBJECTIVE: Does trust in physicians aid or hinder patient autonomy? We examine the relationship between trust in the recipient's doctor, and desire for a participative role in decisions about medical treatment. DESIGN: We conducted a cross-sectional survey in an urban Canadian teaching hospital. SETTING AND PARTICIPANTS: A total of 606 respondents in three clinics (breast cancer, prostate cancer, fracture) completed questionnaires. VARIABLES STUDIED: The instrument included the Problem Solving Decision Making (PSDM) Scale, which used two vignettes (current health condition, chest pain) to categorize respondents by preferred role, and the Trust-in-Physician Scale. RESULTS: Few respondents preferred an autonomous role (2.9% for the current health condition vignette and 1.2% for the chest pain vignette); most preferred shared decision-making (DM) (67.3% current health condition; 48.7% chest pain) or a passive role (29.6% current health condition; 50.1% chest pain). Trust-in-physician yielded 6.3% with blind trust, 36.1% with high trust, 48.6% moderate trust and 9.0% low trust. As hypothesized, autonomous patients had relatively low levels of trust, passive respondents were more likely to have blind trust, while shared respondents had high but not excessive trust. Trust had a significant influence on preferred role even after controlling for the demographic factors such as sex, age and education. CONCLUSIONS: Very few respondents wish an autonomous role; those who do tend to have lower trust in their providers. Familiarity with a clinical condition increases desire for a shared (as opposed to passive) role. Shared DM often accompanies, and may require, a trusting patient-physician relationship.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".