Donations Made and Received: A Study of Disclosure Practices of Pharmaceutical Companies and Patient Groups in Canada
Bibliographic record
Abstract
Given the increasing role of patient groups in pharmaceutical policy-making in Canada, this observational study was undertaken to determine whether companies that are members of Innovative Medicines Canada (IMC) list, on their publicly available websites, the names of patient groups that they make donations to and reciprocally, whether patient groups publicly list the names of the companies that they receive donations from. Websites of IMC members were searched for the names of the patient groups receiving donations, value of the donations and year the donations were made. The website of each patient group that was listed as receiving a donation was then searched for information about the name of companies making donations along with the value of the donations, year the donations were made and percent of the patient groups' income represented by the donation. For donations over $50 000, an attempt was made to match donations that companies made to donations that patient groups received. Eleven of 44 IMC members reported making 165 donations to 114 different patient groups. Seventy-nine of these 114 groups reported receiving 373 donations from IMC members. Information about the value of donations, the year that they were given and received and the percent of patient groups' income that they represented was limited. Donations made and received could not be matched because of the absence of information about the donations. Reporting on websites about donations by both companies and patient groups in Canada is haphazard, inconsistent and incomplete. Reforms are need to both the way that companies and patient groups report donations.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".