Mapping the cancer research landscape across Zambia: evidence to support national cancer control planning
Bibliographic record
Abstract
Background: Zambia faces the double burden of rising cancer incidence and a disproportionate volume of mortality from delayed presentations. The Ministry of Health Zambia acknowledged cancer research as a key pillar of cancer control in the National Cancer Control Strategic Plan 2022-2026, but there remains a paucity of country-specific evidence to inform strategies, implementation, monitoring and evaluation of research activities. Our study aimed to map and critically analyse the existing cancer research landscape to inform national planning. Methods: We adopted a two-stage mixed-method research. First, we conducted a systematic review, including 76 Zambian cancer studies published between 2012 and 2022, adhering to PRISMA guidance. Second, we conducted an in-person modified consensus meeting in Ndola, Zambia attended by 31 domestic and international stakeholders, to co-develop priorities and strategies based on gaps and facilitators identified through the systematic review. Results: = 34/76), respectively. The existing national cervical cancer registry, active global collaboration and adoption of technology were facilitators to be leveraged to build research capacity through multi-level, stakeholder-specific strategies. Conclusion: To strengthen research capacity, sustained commitment to priorities through the implementation of co-developed strategies is required at individual, organisational and institutional levels. This paradigm shift is necessary to deliver evidence-based cancer care tailored to the needs of Zambians with emphasis on value and quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".