Physician and Practice Characteristics Associated with the Early Utilization of New Prescription Drugs
Bibliographic record
Abstract
BACKGROUND: Prescription of new drugs contributes to substantial increases in annual drug expenditures. A small proportion of physicians appear to be early users of new prescription drugs and little is known about their characteristics. OBJECTIVE: To estimate the initial utilization rate of new prescription drugs among physicians, and the physician and practice characteristics associated with early use. DESIGN: Cumulative prospective assessment over a 5 year period (1989-1994) of new drug utilization rates in a randomly selected cohort of Quebec physicians. PARTICIPANTS: 1661 physicians and 669,867 elderly patients. OUTCOME: Prescribing rate of 20 new drugs, in 6 therapeutic categories, to elderly patients in the first 6 months after inclusion in the Quebec formulary. RESULTS: The 20 new drugs were prescribed by 1.3-22.3% of physicians, and there was an 8 to 17-fold difference in new drug utilization rates among prescribers. Characteristics associated with higher rates of utilization differed for general practitioners and specialists. Male general practitioners, and physicians graduating from the most recently established medical school in the province, had higher rates of new drug utilization, whereas recent graduation was only associated with higher utilization rates among specialists. Practice volume was associated with higher rates of utilization among GPs. For both GPs and specialists, having a high proportion of elderly in one's practice and a rural or remote practice location was associated with lower utilization rates. CONCLUSIONS: Physician sex, specialty, medical school, years since graduation, practice location, volume, and relative proportion of elderly in the physician's practice influence the utilization of new drugs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".