National patient groups in Canada and their disclosure of relationships with pharmaceutical companies: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study investigates the information and policies that Canadian patient groups post on their publicly available websites about their relationships with pharmaceutical companies. DESIGN: Cross-sectional study. SETTING: Canadian national patient groups. PARTICIPANTS: Ninety-seven patient groups with publicly available websites. INTERVENTIONS: Each patient group was contacted by email. Information from patient groups' websites was collected about: total annual revenue for the latest fiscal year, year revenue was reported, revenue from pharmaceutical company donors, purpose of the donation, presence of donors' logos on the website and hyperlinks to donors' websites, previous and current employment information about board members and staff, external audits about the group's finances and whether the group endorses products made by donors. Analysis of publicly available policies looking at: board and/or advisory board, acceptance of donations and revenue generation, independence of decision-making, endorsements, assistance to and/or interactions between patient members from a donor or another company/person acting on behalf of a donor and audits/monitoring/compliance. PRIMARY AND SECONDARY OUTCOME MEASURES: Number of patient groups posting information on their websites about their relationships with pharmaceutical companies; the presence and contents of patient group policies covering different topics about relationships with pharmaceutical companies. RESULTS: Fifty-three (54.6%) of 97 groups reported donations from pharmaceutical companies. Forty-one (42.3%) groups showed the logos of pharmaceutical companies on their websites and 22 (53.7%) had hyperlinks to pharmaceutical company websites. Twenty-five (25.8%) of these groups endorsed pharmaceutical products produced by brand-name companies that had donated to the groups. Twenty-six (26.8%) groups had policies that dealt with relationships with pharmaceutical companies. CONCLUSIONS: Pharmaceutical industry funding of the included patient groups was common. Despite this, relatively little information was provided on patient group websites about their relationships with pharmaceutical companies. Only 26 out of 97 groups had publicly available policies that directly dealt with their relationships with pharmaceutical companies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".