Prescription medicines: decision‐making preferences of patients who receive different levels of public subsidy
Bibliographic record
Abstract
OBJECTIVE: To compare the relative importance of medicine attributes and decision-making preferences of patients with higher or lower levels of insurance coverage in a publicly funded health care system. DESIGN AND SETTING: Cross-sectional telephone survey of randomly selected regular medicine users aged ≥18 years in the Hunter Valley, NSW, Australia. MAIN VARIABLES STUDIED: Questions about 27 medicine attributes and active involvement in decisions to start a new medicine. RESULTS: After adjustment, there were few differences between the 408 concession card holders (high insurance) and 410 general beneficiaries (low insurance) in their assessment of the importance of medicine attributes. For both groups, the explanation of treatment options, establishing the need for the medicine, and medicine efficacy and safety were the most important considerations. Medicine costs, the treatment burden and medicine familiarity were less important; the views of family and friends ranked lowest. There was a statistically significantly greater influence of the regular doctor for the concession card holders than general beneficiaries (93.6 vs. 84%, adjusted OR 2.80, 95% CI 1.31, 5.99). Concession card holders were more likely to favour doctors having more say in the decision-making process (crude OR 1.69, 95% CI 1.28, 2.24), and more likely to report the most recent treatment decision being made by the doctor alone, compared with general beneficiaries (61.2 vs. 40.3%). CONCLUSION: Medicine need, efficacy and safety are viewed as paramount for most patients, irrespective of insurance status. While patients report the importance of participation in treatment decisions, delegation of decision making to the doctor was common in practice.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".