Exposure, access and interaction: A global analysis of sponsorship of nursing professional associations
Bibliographic record
Abstract
AIM: To analyse the nature and extent of sponsorship of nursing professional associations and their major scientific conferences. DESIGN: Cross-sectional content analysis. METHODS: Data were extracted from the websites and conference documents of 156 national and international professional nursing associations in 2019 to identify sponsors. Sponsorship prospectuses were analysed to estimate the value and describe the nature of sponsorship arrangements. We analysed sponsorship patterns using social network analysis. RESULTS: Most associations (84/156, 54%) did not report any sponsors. Sponsorship was concentrated among specialty nursing associations in high-income countries. Half of identified sponsors promoted products used in clinical care (50%; 981/1969); the majority represented the medical device industry (69%; 681/981). Top sponsors generally favoured opportunities that promoted interaction with conference attendees. CONCLUSION: Globally, commercial sponsorship of nursing associations is a common, but not the dominant source of support for these activities. Half of sponsors were commercial entities that manufactured or distributed products used during clinical care, which presents a risk of commercial influence over education and ultimately, clinical practice. Sponsors favoured opportunities to interact directly with nurses, determine educational content, or foster continued interaction.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".