Cancer Education in Nigeria: Reflections on a Community-based Intervention by a Physicians’ Association
Bibliographic record
Abstract
BackgroundCancer has become an increasingly important source of morbidity and mortality in Nigeria; however our country lacks an organized cancer control system.Low awareness about the disease spectrum among health professionals (HCP) and health policymakers (HPM) is among the challenges affecting cancer control in Nigeria.This reflection describes the process of providing cancer education in Nigeria to HCP and HPM, through the Cancer Control in Primary Care (CCPC) course.It also shares our experiences during the planning, and challenges encountered. Methods MedicalWomen's Association of Nigeria partnered with American Society of Clinical Oncology to deliver cancer education in Akwa Ibom State of Nigeria in February 2016.The main learning objectives were: • Provide HCP working in Akwa Ibom State with essential knowledge on cancer control • Provide evidence-based management strategies • Promote multidisciplinary approach for managing breast and cervical cancers • Promote the formulation of a cancer control policy in the state • Share knowledge and experiences with others working in the field Course lasted 3 days, and featured didactic lectures (n=11); demonstrations and simulations (n=4); and plenary sessions (n=7).Course was planned using emails, phone calls, WhatsApp® chats and text messages. ResultsCourse was successful with a daily attendance of >140 participants comprising physicians, nurses and policymakers in primary, secondary, tertiary and private health facilities in the state.Over 97% of the participants had improved their knowledge of cancers through the course.We also identified local priorities for cancer control.Use of multiple approaches to recruitment and funding, as well as working with various local partners were crucial to our success. ConclusionChallenges encountered in providing cancer education through this medium include funding, recruitment of participants and event management.Overall, the use of the CCPC course to improve cancer education has proven to be successful, cost-effective and important in building practice networks among HCP and HPM in Akwa Ibom State.We recommend this approach for improving cancer education in resource-limited settings.Outcome of course evaluation will be shared in a different communication.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| 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".