Developing a partnership to improve health care delivery to children <18 years with cancer and blood disorders in the English-speaking Caribbean: lessons from the SickKids-Caribbean Initiative (SCI)
Bibliographic record
Abstract
In 2013, the SickKids-Caribbean Initiative (SCI) was formalised among The Hospital for Sick Children in Toronto, Canada, the University of the West Indies, and Ministries of Health in six Caribbean countries (Barbados, The Bahamas, Jamaica, St. Lucia, St. Vincent and the Grenadines, and Trinidad and Tobago). The aim was to improve the outcomes and quality of life of children (<18 years) with cancer and blood disorders in the partner countries. Core activities included filling a human resource gap by training paediatric haematologists/oncologists and specialised registered nurses; improving capacity to diagnose and treat diverse haematology/oncology cases; developing and maintaining paediatric oncology databases; creating ongoing advocacy activities with international agencies, decision makers, and civil society; and establishing an integrated administration, management, and funding structure. We describe core program components, successes, and challenges to inform others seeking to improve health service delivery in a multidisciplinary and complex partnership.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".