Feasibility and clinical and implementation effectiveness of an adapted early warning signs and symptoms intervention for the early detection of childhood cancer in Cameroon and Kenya: protocol for a quasi-experimental, hybrid type 2 implementation effectiveness study
Bibliographic record
Abstract
INTRODUCTION: Childhood cancer accounts for a significant proportion of global childhood mortality, especially in low-income and middle-income countries (LMICs). Unlike many adult malignancies, primary prevention of childhood cancers is not possible. Improving survival requires a two-pronged strategy: earlier diagnosis and effective treatment. Our study aims to establish the feasibility, clinical and implementation effectiveness of an adapted early warning signs and symptoms (EWSS) intervention in Cameroon and Kenya. It will equip healthcare workers, Ministry of Health (MOH) representatives and National Cancer Institute leaders with evidence-informed guidance on implementing context-adapted interventions to improve the early detection and referral of childhood cancers in these countries. METHODS AND ANALYSIS: The study is a quasi-experimental, hybrid type 2 implementation effectiveness study based on a Ghanaian adaptation of the 'Saint Siluan' EWSS campaign. Our protocol proposes context-specific adaptation and evidence-based implementation of the EWSS intervention through iterative engagement with country-level implementation teams to train healthcare workers and improve referral pathways for earlier childhood cancer diagnoses in each study country. Training effectiveness will be measured through pretraining and post-training tests of knowledge and application, as well as training satisfaction surveys. Clinical effectiveness will be assessed by using a REDCap database to track the number of newly diagnosed childhood cancer cases in the study regions and counties, healthcare timelines and paths to diagnosis, and the stage and proportion of metastatic disease at diagnosis. Implementation effectiveness will be evaluated through interviews with senior and mid-level health system partners and clinicians, tracking fidelity to the implementation process as laid out in The Implementation Roadmap Workbook, and analysis of meeting minutes from monthly local implementation team meetings. ETHICS AND DISSEMINATION: This study has received ethical approval from The Hospital for Sick Children (REB # 1000080092) and all participating sites. We have received National Ethical Clearance from the Cameroon Ethical Board (#1699) and Regional Administrative Authorizations from our piloting regions (Centre and West). We have also received ethical clearance from Kenyatta National Hospital (KNH) (ERB# KNH-ERC/RR/955) and our National Commission for Science, Technology and Innovation in Kenya licence from the counties we are piloting in Kenya. As clinical data will be collected from existing health registries and patient charts, patient consent will not be required; however, we will obtain consent from all members of the leadership implementation teams and operational implementation teams for their participation in the implementation meetings and from all individuals participating in the semistructured interviews. We will disseminate findings to build awareness and share findings among various target audiences: (1) key county and regional parties (eg, clinical societies, advocacy groups, country MOHs and regional bodies such as the East African Community, Economic Community of West African States); (2) international bodies such as the WHO; and (3) the academic community.
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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 | 0.045 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".