Who Is Portrayed in Psychotropic Drug Advertisements?
Bibliographic record
Abstract
The purpose of our study was to determine who is portrayed in psychotropic drug advertisements across time in three national psychiatric journals. All psychotropic drug advertisements portraying people were collected from the American Journal of Psychiatry, the British Journal of Psychiatry, and the Canadian Journal of Psychiatry at three time intervals (1981, 1991, and 2001). The advertisements were classified according to patient demographics, patient portrayal, and product information. Chi-square analysis was used to test for statistically significant associations among the variables. Fifty-seven percent of the psychotropic drug advertisements featured women, and 88% portrayed white patients. Statistically significant associations were detected between gender and the setting in which the patient was portrayed (chi(2) = 13.54, df = 3, p < 0.004), and gender and role (chi(2) = 29.41, df = 3, p < 0.001). Disproportionate gender representation was most notable in the 2001 time interval in the American Journal of Psychiatry. Women and white patients were overrepresented compared with psychiatric epidemiologic data in all three countries. The effect of these advertisements on physician perception, diagnosis, and prescribing is unknown but may be substantial. Future advertisements for psychotropic drugs should seek more balanced representations of gender and race.
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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 | 0.003 | 0.015 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".