Policy strategies for capacity building and scale up of the workforce for comprehensive cancer care: a systematic review
Bibliographic record
Abstract
•Shortages of the oncology workforce are common in low- and middle-income countries (LMICs).•Strategies for capacity building of the cancer workforce must be evidence based and impact oriented.•Most common strategies to improve capacity are educational and aim at increasing the number of providers.•Organizational approaches, such as role delegation and digital health solutions, are key to improving the workforce capacity.•More efforts are needed toward accountability and monitoring, and to enhance the retention of the workforce in LMICs. BackgroundPatients 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 methodsWe 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).ResultsThe 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.ConclusionsWorkforce 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. 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. 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). 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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".