Sponsorship of Australian and New Zealand medical societies by healthcare companies: an observational study
Bibliographic record
Abstract
Objectives: To examine sponsorship of Australian and New Zealand medical societies by healthcare companies and whether societies have policies to deal with conflicts of interest. Design: Cross-sectional study conducted in March 2022. Setting: Australia and New Zealand. Participants: Medical societies in both countries. Main outcome measures: The percent of medical societies that list sponsorship from healthcare companies on either their home webpages or the webpages of their annual meetings and/or that issue prospectuses to potential sponsors. The percent of societies with sponsorship that also have policies about their interactions with their sponsors. Whether societies feature their sponsors' logos on their webpages and have hyperlinks to sponsors' webpages and what percent of societies' annual revenue comes from sponsorships. Results: Ninety-two medical societies were identified. Sixty-two had healthcare company sponsorship and 10 of the societies with sponsorship had policies to deal with interactions with their sponsors. Fifty-four societies displayed the logos of their sponsors on their home webpages and/or the webpages of their annual meetings. Only 6 societies provided enough information to calculate what percent of their revenue comes from sponsorships. For 5 of the 6 the percent was well below 50%. Conclusions: The acceptance of sponsorships from healthcare companies by Australian and New Zealand societies is common and few societies have policies to deal with these relationships. In general, societies appear to get only a small percent of their annual revenue from sponsorships.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".