The Effects of Direct-to-Consumer-Advertising on Patients in a Northern Canadian Community: A Cross-Sectional Survey
Bibliographic record
Abstract
Background: Previous studies have investigated the effects of direct-to-consumer advertising (DTCA) of prescription medication in the United States and in a large Canadian centre close to the US border. This study examined how DTCA affects patients in a smaller, more remote Canadian community. Methods: A cross-sectional survey of patients ≥18 years was conducted in 8 primary care practices in Prince George, BC. Main outcome measures were past and present requests for advertised and non-advertised prescription medications. Results: Of 435 eligible patients, 217 (49.8%) agreed to be surveyed; 209 questionnaires were included in the final analysis. Patients with high exposure to DTCA were significantly more likely to be under the age of 50 ( p = 0.016). A total of 1.6% of patients with high DTCA exposure versus 0% with low exposure requested advertised medications during the study ( p = 0.26), and 4.8% versus 0%, respectively, had requested advertised medications in the past ( p = 0.052). The percentage of patients requesting any prescription medication, advertised or non-advertised, was 10.2% in the high DTCA exposure group, compared to 4.9% in the low exposure group. (OR = 2.18, 95% CI 0.69-6.93). Discussion: The rate of requests for advertised prescription medications in this northern Canadian community was lower than rates reported from larger, less-remote centres. However, exposure to DTCA remained surprisingly high, especially in younger patients. Results suggest that patients with higher self-reported exposure to DTCA may be more likely to request both advertised and non-advertised prescription medications. Conclusions: All health care professionals with a prescribing role should be aware of the potential impact of DTCA and ensure that patients are receiving objective, evidence-based information that will enable them to make fully informed decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".