COMMUNITY ENGAGED APPROACH TO CANCER CONTROL POLICY IN ABIA STATE - NIGERIA: A MIXED-METHODS ACTION RESEARCH PROJECT
Bibliographic record
Abstract
Background\nCancers are becoming increasingly common in Nigeria and other developing countries. The most common cancers in the country are those affecting the breast, cervix, and prostate. Beyond the National Cancer Control Plan, most States in Nigeria do not have State cancer control policies which is unlike the situation in Canada and other developed countries. Using the multiple perspective analysis framework, this research sought to explore the perspectives of patients diagnosed with cancer, healthcare providers and health policymakers regarding cancer policy in Abia State.\n\nMethods\nA concurrent mixed methods action research design was used. Sampling included individuals aged ≥18 years who were diagnosed with breast or cervical cancer, provided cancer treatment or made health policy in the State. This study was conducted in collaboration with the Abia Cancer Control Group (ACCG), a community-based coalition of non-governmental organizations, clinicians, and government parastatals. Survey data were collected at the same time as the interviews which occurred following ethical approvals from the University of Saskatchewan’s Behavioural Research Ethics Board and Abia State’s Ministry of Health Human Research Ethics Committee.\n\nResults/Findings\nSurvey participants were 29 patients who had been diagnosed with cancer, 50 health care providers and 33 policymakers (n=112), with an average age of 45 (±11) years. Challenges identified by ≥60% of participants were: lack of local data regarding cancers (95.2%, 79/83); lack of treatment pathways (92.8%, 77/83); absence of support groups for patients (88.0%, 73/83); low public awareness (75.9%, 63/83); and limited availability of treatment options (62.6%, 52/83). Some themes that evolved from the qualitative data were: low cancer awareness; delays in cancer treatment; and, financial burden on patients. The top three priority areas for a new cancer control policy were: cancer prevention (83%, 93/112); State cancer legislation (80%, 86/112); and multi-agency partnerships (79%, 88/112). Most participants (80%, 90/112) recommended that health insurance should fund ≥16% of cancer control activities, although policymakers were more likely to make quarterly insurance contributions than patients (7 out of 10 vs. 5 out of 10). Data from participants that agreed to be interviewed (n=24) were grouped into the following themes: Experiences (e.g. challenges regarding cancer prevention, awareness of early detection, delays in cancer service, and cost of services) and Expectations (e.g. priority rating for cancer control, funding structure, and framework for a future cancer control policy). ACCG provided contextual evidence of the usefulness of these findings by organizing community-driven cancer control projects locally linked to advocacy, training of clinicians, patient navigation and support, as well as developing a centralized cancer reporting system.\n\nConclusion\nCancer control was an important issue for all populations. Inadequate early detection services with a background of >3-month diagnostic delay characterized cancer control in Abia State. Future cancer control policy should emphasize: cancer prevention; the creation of local clinical pathways; and, a blended model for financing cancer control activities. Collaboration with community groups such as ACCG will be critical to the successful development and implementation of a cancer control policy in Abia State.
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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 | 0.037 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".