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 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 | 0.176 | 0.350 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".