Using multiple perspectives analysis to propose state cancer control policy in Abia State, Nigeria.
Bibliographic record
Abstract
e14132 Background: Beyond the National Cancer Control Plan, most States in Nigeria do not have State cancer control policies. Using the multiple perspectives 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. Methods: A concurrent mixed methods action research design was used. Sample included individuals aged ≥18 years who were diagnosed with cancer (patients), provided cancer treatment (providers) or made health policy (policymakers) in the State. Data were collected using surveys and key informant interviews. Data analysis involved descriptive statistics, chi-square test and a deductive thematic analysis. Results: Participants were 29 patients, 50 providers and 33 policymakers (n = 122), with an average age of 45 (±11) years. Challenges identified by ≥60% of participants were: low public awareness (75.9%); limited availability of treatment options (62.6%); lack of treatment pathways (92.8%); lack of local cancer data (95.2%); and, absence of support groups for patients (88.0%). Each group of participants (n = 3) rated cancer 9 out of 10 as a public health priority. Some qualitative themes were: low cancer awareness, delays in cancer treatment and financial burden on patients. “One thing I’d like to include in the policy is to make cancer a reportable disease in the State.” -Policymaker. 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). Conclusions: Cancer control was an important issue for people in the State. Inadequate prevention services with a background of > 3-month diagnostic delays, characterized cancer control in Abia State. Future cancer control policy should emphasize cancer prevention, the creation of local clinical pathways and a blended financing model. [Table: see text]
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".