Impact of Pharmaceutical Industry Versus University Sponsorship on Survey Response: A Randomized Trial among Canadian Hepatitis C Care Providers
Bibliographic record
Abstract
BACKGROUND: Surveys originating from universities appear to have higher response rates than those from commercial sources. In light of the growing scrutiny placed on physician-industry relations, the present study aimed to determine the impact of the pharmaceutical industry versus university sponsorship on response to a postal survey completed by Canadian hepatitis C virus (HCV) care providers. PATIENTS AND METHODS: In the present controlled trial, 229 physicians and nurses involved in HCV treatment were randomly assigned to receive a survey with sponsorship from a pharmaceutical company or university. The primary outcome was the proportion of completed surveys returned. The secondary outcomes included the response rate after the first mailing and the number of days taken to respond. RESULTS: One hundred fifteen participants were randomly assigned to receive the pharmaceutical industry survey and 114 were assigned to receive the university survey. The final response rate was 72.9% (167 of 229), which did not differ between the industry and university groups (RR=0.91; 95% CI 0.78 to 1.07). Nurses (OR=2.20; 95% CI 1.08 to 4.48) and participants from an academic centre (OR=3.14; 95% CI 1.64 to 6.00) were more likely to respond. The response rate after the first mailing (RR=0.85; 95% CI 0.68 to 1.07) and the median number of days taken to respond (21 days in both groups; P=0.20) did not differ between the industry and university groups. CONCLUSIONS: Pharmaceutical industry sponsorship does not appear to negatively impact response rates to a postal survey completed by Canadian HCV care providers.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".