Gender bias in cardiovascular advertisements
Bibliographic record
Abstract
RATIONALE: Women with cardiovascular disease are treated less aggressively than men. The reasons for this disparity are unclear. Pharmaceutical advertisements may influence physician practices and patient care. AIMS AND OBJECTIVE: To determine if female and male patients are equally likely to be featured in cardiovascular advertisements. METHODS: We examined all cardiovascular advertisements from US editions of general medical and cardiovascular journals published between 1 January 1996 and 30 June 1998. For each unique advertisement, we recorded the total number of journal appearances and the number of appearances in journals' premium positions. We noted the gender, age, race and role of both the primary figure and the majority of people featured in the advertisement. RESULTS: Nine hundred and nineteen unique cardiovascular advertisements were identified of which 254 depicted a patient as the primary figure. A total of 20%[95% confidence interval (CI) 15.3-25.5%] of these advertisements portrayed a female patient, while 80% (95% CI 74.5-84.7%) depicted a male patient, P <0.0001. Female patient advertisements appeared 249 times (13.3%; 95% CI 8.6-18.9%) while male patient advertisements appeared 1618 times (86.7%; 95% CI 81.1-91.4%), P <0.0001. Female patient advertisements also had significantly fewer mean appearances than male patient advertisements in journals' premium positions (0.82 vs. 1.99, P=0.02). Similar results were seen when the advertisements were analysed according to predominant gender. CONCLUSIONS: Despite increasing emphasis on cardiovascular disease in women, significant under-representation of female patients exists in cardiovascular advertisements. Physicians should be cognizant of this gender bias.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".