Distribution of the workforce involved in cancer care: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: A skilled health workforce is instrumental for the delivery of multidisciplinary cancer care and in turn a critical component of the health systems. There is, however, a paucity of data on the vast inequalities in cancer workforce distribution, globally. The aim of this study is to describe the global distribution and density of the health care workforce involved in multidisciplinary cancer management. METHODS: We carried out a systematic review of the literature to determine ratios of health workers in each occupation involved in cancer care per 100 000 population and per 100 cancer patients (PROSPERO: protocol CRD42018095414). RESULTS: We identified 33 eligible papers; a majority were cross-sectional surveys (n = 16). The analysis of the ratios of health providers per population and per patients revealed deep gaps across the income areas, with gradients of workforce density, highest in high-income countries versus low-income areas. Benchmark estimates of optimal workforce availability were provided in a secondary research analysis: mainly high-income countries reported workforce capacities closer to benchmark estimates. A paucity of literature was defined for critical health providers, including for pediatric oncology, surgical oncology, and cancer nurses. CONCLUSION: The availability and distribution of the cancer workforce is heterogeneous, and wide gaps are described worldwide. This is the first systematic review on this topic. These results can inform policy formulation and modelling for capacity building and scaleup.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".