Quality of advertisements for prescription drugs in family practice medical journals published in Australia, Canada and the USA with different regulatory controls: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To assess if different forms of regulation lead to differences in the quality of journal advertisements. DESIGN: Cross-sectional study. PARTICIPANTS: Thirty advertisements from family practice journals published from 2013 to 2015 were extracted for three countries with distinct regulatory pharmaceutical promotion systems: Australia, Canada and the USA. PRIMARY AND SECONDARY OUTCOME MEASURES: Advertisements under each regulatory system were compared concerning three domains: information included in the advertisement, references to scientific evidence and pictorial appeals and portrayals. An overall ranking for advertisement quality among countries was determined using the first two domains as the information assessed has been associated with more appropriate prescribing. RESULTS: Advertisements varied significantly for number of claims with quantitative benefit (Australia: 0.0 (0.0-3.0); Canada: 0.0 (0.0-5.0); USA: 1.0 (0.0-6.0); p=0.01); statistical method used in reporting benefit (relative risk reduction, absolute risk reduction and number needed to treat; Australia: 6.7%, n=2; Canada: 10.0%, n=3; USA: 36.6%, n=11; p=0.02); mention of adverse effects, warnings or contraindications (Australia: 13.3%, n=4; Canada: 23.3%, n=7; USA: 53.3%, n=16; p=0.002); equal prominence between safety and benefit information (Australia: 25.0%, n=1; Canada: 28.6%, n=2; USA: 75.0%, n=12; p=0.04); and methodological quality of references score (Australia: 0.4150 (0.25-0.70); Canada: 0.25 (0.00-0.63); USA: 0.25 (0.00-0.75); p<0.001). The USA ranked first, Canada second and Australia third for overall quality of journal advertisements. Significant differences for humour appeals (Australia: 3.3%, n=1; Canada: 13.3%, n=4; USA: 26.7%, n=8; p=0.04), positive emotional appeals (Australia: 26.7%, n=8; Canada: 60.0%, n=18; USA: 50.0%, n=15; p=0.03), social approval portrayals (Australia: 0.0%, n=0; Canada: 0.0%, n=0; USA: 10.0%, n=3; p=0.04) and lifestyle or work portrayals (Australia: 43.3%, n=13; Canada: 50.0%, n=15; USA: 76.7%, n=23; p=0.02) were found among countries. CONCLUSIONS: Different regulatory systems influence journal advertisement quality concerning all measured domains. However, differences may also be attributed to other regulatory, legal, cultural or health system factors unique to each country.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".