Exploring physician gender bias in the initiation of prescribing cascades for older men and women: a qualitative clinical vignette study protocol
Bibliographic record
Abstract
INTRODUCTION: A prescribing cascade occurs when a drug is prescribed to manage the often unrecognised side effect of another drug; these cascades are of particular concern for older adults who are at heightened risk for drug-related harm. It is unknown whether, and to what extent, gender bias influences physician decision-making in the context of prescribing cascades. The aim of this transnational study is to explore the potential impact of physician implicit gender biases on prescribing decisions that may lead to the initiation of prescribing cascades in older men and women in two countries, namely: Canada and Italy. METHODS AND ANALYSIS: Male and female primary care physicians at each site will be randomised 1:1 to a case vignette that features either a male or female older patient who presents with concerns consistent with the side effect of a medication they are taking. During individual interviews, while masked to the true purpose of the study, participants will read the vignette and use the think-aloud method to describe their ongoing thought processes as they consider the patient's concerns and determine a course of action. Interviews will be recorded, transcribed verbatim and thematic analysis will be conducted to highlight differences in decisions in the interviews/transcripts, using a common analytical framework across the sites. ETHICS AND DISSEMINATION: This study has received ethics approval at each study site. Verbal informed consent will be received from participants prior to data collection and all data will be deidentified and stored on password-protected servers. Results of this study will be disseminated through peer-reviewed journal articles and presented at relevant national and international conferences.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".