A Report from a Community-Centric Cancer Control Approach in the Post-Conflict Northern Province of Sri Lanka
Bibliographic record
Abstract
Late-stage cancer diagnoses of prevalent cancers are increasing in the Northern Province of Sri Lanka, a region currently rebuilding its healthcare system after a prolonged civil war. In this region, cancer prevention services are limited. We describe a community-centric approach to cancer education and prevention as a strategy to cancer control in this rural, post-conflict region. Nursing students were trained as Community Cancer Educators (CCEs), equipping them with essential knowledge about cancer symptoms, risk factors, and the importance of early detection. The training also included creative methods such as dance and drama to help CCEs communicate cancer-related messages in an engaging and culturally relevant manner. These CCEs supported the oncologist-led community health camps in delivering cancer education and screening directly to community members within their community. We planned the health camps in collaboration with the existing community-based public health system for better outreach. Feedback from community participants and healthcare providers suggests that this community-centric approach can improve cancer awareness, encourage participation in population screening, and support early cancer detection. This approach could strengthen community engagement and contribute to more equitable access to prevention and screening services in rural, post-conflict settings with limited healthcare infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".