Are patients' decision‐making preferences being met?
Bibliographic record
Abstract
OBJECTIVE: To investigate the information and decision-making expectations of general practice patients during real life consultations. DESIGN: Post-consultation, quantitative patient preference and enablement questionnaire. SETTING AND PARTICIPANTS: Patients attending for routine appointments in general practice surgeries in Oxfordshire, UK. RESULTS: Thirteen Oxfordshire general practitioners (GPs) volunteered to take part and a total of 171 patients completed and returned the questionnaire. Between a quarter and one-third of patients reported receiving less information than they desired, particularly in relation to the risks and benefits of medical treatments. Patients who preferred the doctor to make decisions for them (35%), were more likely to have their preferences met (64%) compared with patients wishing to share decisions (47%) or make their own (18%) who were less likely to achieve this role (52 and 41%, respectively). However, it could not be demonstrated unequivocally that these differences were statistically significant. In total, 61% of patients perceived that they achieved their preferred decision-making role. No significant differences were found in post-consultation enablement scores between any of the decision preference groups. Patients' assessments indicated that some doctors were more successful at achieving congruence than others. CONCLUSION: The decision-making preferences of general practice patients tend to vary. However, there was a substantial mismatch between the stated preferences of patients for the role they wanted to have in decision-making and what they felt actually took place in their consultation. Therefore, it remains a challenge for doctors to match their consultation style to the decision-making preferences of individual patients.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".