Influence of Physician Sex and Gender on Prescribing Practices Among Older Adults
Bibliographic record
Abstract
Although prescribing is the most common intervention provided by physicians, limited research has examined the role of physician sex and gender on prescribing practices. In this article, we briefly summarize research relating to differences in prescribing behaviors based on physician sex and gender. To identify articles, PubMed was searched for studies from the last 20 years reporting on prescribing differences by physician sex or gender for the general population and specifically for older adults. We describe major themes emerging from the studies, illustrate findings from key studies, and note the major gaps in the literature, notably the lack of evidence on prescribing for older adults. Given the paucity of research in this area, we also explore evidence on the impact of physician sex and gender on other aspects of healthcare delivery, such as communication within the patient-physician relationship, and consider how these findings may also apply to prescribing behaviors. In general, we note that female physicians have been observed to engage in more careful and conservative healthcare provision including prescribing. A careful and conservative approach to prescribing may reduce the incidence of adverse drug events in older adults and be linked to a more patient-centered approach to care. To what extent these differences in prescribing are important for patient health outcomes is unknown, and further research is required to identify optimal prescribing practices that minimize harms.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".