Portrayal of female physicians in cardiovascular advertisements.
Bibliographic record
Abstract
BACKGROUND: Despite increasing numbers of female medical school graduates, few women enter cardiovascular specialties. Pharmaceutical promotion may influence physician behaviour. It is unclear how female physicians are represented in cardiovascular advertisements, which may, in turn, influence physician perceptions. OBJECTIVES: To determine if female and male physicians are equally represented in cardiovascular advertisements. METHODS: All cardiovascular advertisements from American editions of general medical and cardiovascular journals published between January 1, 1996, and June 30, 1998, were examined. For each unique advertisement, the total number of journal appearances and the number of appearances in journals' premium positions were recorded. The role, sex, age and race of the primary figure featured in the advertisement were noted. RESULTS: Nine hundred nineteen unique advertisements were identified, 35 of which depicted a physician as the primary figure. Six (17%, 95% CI 8.1% to 32.7%) of these advertisements portrayed a female physician, while 29 (83%, 95% CI 67.3% to 91.9%) depicted a male physician (P<0.001). Female physician advertisements appeared in journals 39 times (20.7%; 95% CI 2.8% to 43.5%), while male physician advertisements appeared 149 times (79.3%; 95% CI 56.5% to 97.2%) (P=0.01). The odds ratio for a female physician advertisement appearing in a premium position compared with a male physician advertisement was 0.25 (95% CI 0.09 to 0.68). CONCLUSION: The relative paucity of female physicians in cardiovascular advertisements is a concern because it may both reflect and reinforce sex asymmetries in cardiovascular specialties.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.001 |
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".