Stepping Stones to Sustainability Within Cancer Clinical Trials in Ireland
Bibliographic record
Abstract
Cancer clinical trials contribute significantly to healthcare-related greenhouse gas emissions, highlighting the need to address sustainability in this area as the climate crisis intensifies. This study provides the first national assessment of sustainability awareness, attitudes, and practices within the Irish cancer clinical trials community. A 21-item cross-sectional survey was distributed to 613 cancer research professionals affiliated with Cancer Trials Ireland, including clinicians, research nurses, trial coordinators, patient advocates and industry staff, yielding a 20.6% response rate. Survey items assessed awareness of sustainability tools, perceived carbon contributors, training received, confidence in implementing green practices, and perceived barriers and enablers to sustainability. Awareness of existing carbon footprint tools was low, with only 21% familiar with the Sustainable Clinical Trials Group guidelines and fewer than 6% aware of the National Institute for Health and Care Research calculator. Despite limited training and low confidence in implementing carbon-reductive measures, 86% of respondents expressed willingness to engage with sustainability initiatives. Trial-related travel, sample kit waste, and trial set-up were perceived as the highest contributors to emissions, though perceptions did not always align with published data. Key barriers included lack of education, institutional support, and regulatory clarity, while financial incentives and training were identified as enablers. Coordinated, system-wide interventions are needed to embed sustainability into cancer clinical trial design, governance, and funding processes.
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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 | 0.293 | 0.285 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".