Childhood Cancer in Cameroon and Kenya: Preliminary findings of an implementation effectiveness study for early detection of childhood cancer.
Bibliographic record
Abstract
e23096 Background: Early childhood cancer detection reduces mortality, particularly in LMIC contexts where an improved understanding of early warning signs and symptoms (EWSS) and integration of vital referral pathways into existing health systems are crucial to improved childhood cancer outcomes. Our project seeks to adapt and implement a Ghanaian-developed EWSS intervention for Kenya and Cameroon using implementation science frameworks to move beyond the common but ineffective train-and-hope approach. Cameroon operates on a three-tiered sub-sector health system, with the intermediate level consisting of regional delegations that support districts1. In Kenya a six-tiered health system delivers primary care (1-2), mid-level care (3-4), advanced care with centers of excellence (5-6) 2. Health system contexts will inform implementation planning and execution. Methods: Health system stakeholders convened in each country to launch their EWSS initiative. Two-day meetings were collaboratively led by local stakeholders and the research team, guided by The Implementation Roadmap3, a multi-implementation framework resource. They discussed l) local barriers to childhood cancer detection, 2) referral pathways, 3) leadership and operational implementation teams, 4) sustainable EWSS training, and 4) target settings. Results: Stakeholders endorsed the EWSS program and identified individuals to form implementation teams to plan and execute implementation reflective of health system organization and realities. Both countries engaged in an evidence-based implementation planning process, reviewed EWSS core components and training logistics, and endorsed a sustainable tiered training model targeting clinicians and oncologists across system levels and institutional providers. Training content will be adapted for country, region, and county contexts. Both countries endorsed and identified district childhood cancer champions to facilitate training and coordinate timely referrals. Common implementation barriers identified included high healthcare worker turnover, transportation logistics for in-person training, and remuneration options for trainees. Conclusions: Health system leaders in Kenya and Cameroon endorsed an evidence-based EWSS implementation and sustainment approach and identified adaptations responsive to the health system contexts. Implementation will be locally led to ensure effectiveness, sustainability, and appropriate cultural EWSS adaptation. Moving forward, local implementation teams will work with researcher-facilitators to implement EWSS toward improved cancer detection rates.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".