Protocol for a multi-country investigation of overseas care and cancer survivorship in small islands developing states of the Eastern Caribbean: The CaSIDEC study
Bibliographic record
Abstract
ABSTRACT Background Small Island Developing States (SIDS) make up worldwide, 65 million people. SIDS have some of the highest rates of cancer mortality burden in the developing world. Disparities in cancer mortality could be attributed to reliance on services overseas which is a common feature across SIDS due to limited resources for comprehensive cancer care on-island. Overseas travel for cancer care, remains largely understudied in SIDS, and epidemiological data on the impact on patient outcomes are lacking. We aim to investigate the association with overseas travel for care and known determinants of patient outcomes (treatment delays, lifestyle and social support) in Caribbean SIDS using mixed-methods. Methods We will establish a cohort of 900 cancer survivors residing in the islands of Antigua, Dominica, Grenada, Saint Kitts, Saint Lucia and Saint Vincent. Eligible participants will be adult cancer survivors (any cancer site, histology and diagnosis year) and having accessed health services in their island of residence due to cancer. Sites for recruitment will be cancer support groups, public and private hospitals and oncology clinics. Participants will be contacted by a trained interviewer for a face-to-face survey. For every country visited for diagnosis and/or treatment, we will record the services accessed, and motives for the choice of country. In-depth interviews and focus groups will be conducted with cancer survivors and their caregivers to learn about overseas travel and social support systems. Discussion This is the first investigation on the influence of overseas travel on cancer care in multiple Caribbean SIDS. Our work will produce recommendations to fill performance gaps in the systems for cancer care in the OECS that will improve patients’ lives at multiple phases. It is an important first step to developing data resources for high-quality research in understudied SIDS.
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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 | 0.047 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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".