Abstract 45: A Think Tank Approach for Inter-Disciplinary Research in Small Island States to Address Gaps in Cancer Control
Bibliographic record
Abstract
Abstract Purpose: Small island developing states (SIDS) experience some of the highest cancer mortality rates worldwide. These high rates are thought to be attributed to features inherent to SIDS e.g. the frequent need to travel overseas for cancer care. Previous cancer control efforts were done in silos and failed to fully address this disproportionate mortality with the Organisation of Eastern Caribbean States (OECS). We present lessons learnt from a new model for inter-disciplinary research on cancer control in the OECS. Methods: From Jan 2023–Sept 2024, the Vaughan A. Lewis Institute for Research & Innovation (Saint Lucia) established a project steering committee (PSC) for the CaSIDEC study. CaSIDEC is an epidemiological study on the impact of overseas travel on cancer care in the SIDS of the Eastern Caribbean. OECS residents and nationals from the diaspora were invited to the PSC based on expertise and influence in the OECS. The PSC functions as a “think tank” aiming to fill critical gaps in evidence- based policy in regional cancer control. With regional policymakers, our PSC addresses 3 areas, (1) collection of new data, (2) analysis of existing regional data and synthesis of scientific evidence, and (3) translation of our study findings to change practice at different levels. Results: After 3 virtual meetings, we identified common objectives among members and agreed on a vision and a governance structure. Our group comprises the 18 co-investigators of the CaSIDEC study (7 researchers, 4 cancer survivors, 4 specialist clinicians, 6 cancer support groups, 3 senior technical representatives from the Ministries of health), 2 research assistants and a graduate student. Every trimester, we hold interactive teleconferences to promote new perspectives on cancer related issues across the OECS. We commenced interviews for the CaSIDEC study (n = 5) to pilot our data collection protocols. We also produced a manuscript reviewing the cancer control initiatives in the Eastern Caribbean to present at a regional conference. We are currently brainstorming on strategies to accelerate changes in policy within the OECS. Conclusion: Creating a think tank that leverages CaSIDEC’s PSC has contributed to the first steps in building significant infrastructure to enhance innovation in cancer control in the OECS. Our model facilitates synergism among diverse stakeholders for participatory research on cancer care across 6 SIDS while simultaneously stimulating knowledge translation. Citation Format: Aviane Auguste, Yvonne Alexander-Akins, Eunetta Bird, Ariane Duncan, Owen Gabriel, Celine Heskey, Lyndelle LeBruin, Janielle P. Maynard, Marcus Natta, Sonia Nixon, Dorothy Phillip, Jacqui Quinn, Nick Shillingford, Steve D. Whittaker, Tami Williams, Hanybal Yazigi, Stephen B. Johnson, Asia Blackman, Tricia Black, Lindonne Telesford. A Think Tank Approach for Inter-Disciplinary Research in Small Island States to Address Gaps in Cancer Control [abstract]. In: Proceedings of the 13th Annual Symposium on Global Cancer Research; 2025 Sep 16. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(12_Suppl):Abstract nr 45.
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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 | 0.209 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".