Policy strategies for capacity building and scale up of the workforce for comprehensive cancer care: a systematic review
Bibliographic record
Abstract
BACKGROUND: Patients with cancer in low- and middle-income countries experience worse outcomes as a result of the limited capacity of health systems to deliver comprehensive cancer care. The health workforce is a key component of health systems; however, deep gaps exist in the availability and accessibility of cancer care providers. MATERIALS AND METHODS: We carried out a systematic review of the literature evaluating the strategies for capacity building of the cancer workforce. We studied how the policy strategies addressed the availability, accessibility, acceptability, and quality (AAAQ) of the workforce. We used a strategic planning framework (SWOT: strengths, weaknesses, opportunities, threats) to identify actionable areas of capacity building. We contextualized our findings based on the WHO 2030 Global Strategy on Human Resources for Health, evaluating how they can ultimately be framed in a labour market approach and inform strategies to improve the capacity of the workforce (PROSPERO: CRD42020109377). RESULTS: The systematic review of the literature yielded 9617 records, and we selected 45 eligible papers for data extraction. The workforce interventions identified were delivered mostly in the African and American Regions, and in two-thirds of cases, in high-income countries. Many strategies have been shown to increase the number of competent oncology providers. Optimization of the existing workforce through role delegation and digital health interventions was reported as a short- to mid-term solution to optimize cancer care, through quality-oriented, efficiency-improving, and acceptability-enforcing workforce strategies. The increased workload alone was potentially detrimental. The literature on retaining the workforce and reducing brain drain or attrition in underserved areas was commonly limited. CONCLUSIONS: Workforce capacity building is not only a quantitative problem but can also be addressed through quality-oriented, organizational, and managerial solutions of human resources. The delivery of comprehensive, acceptable, and impact-oriented cancer care requires an available, accessible, and competent workforce for comprehensive cancer care. Efficiency-improving strategies may be instrumental for capacity building in resource-constrained settings.
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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 | 0.040 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".