Oncology training and education initiatives in low and middle income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: The global cancer burden falls disproportionately on low and middle-income countries (LMICs). One significant barrier to adequate cancer control in these countries is the lack of an adequately trained oncology workforce. Oncology education and training initiatives are a critical component of building the workforce. We performed a scoping review of published training and education initiatives for health professionals in LMICs to understand the strategies used to train the global oncology workforce. METHODS: We searched Ovid MEDLINE and Embase from database inception (1947) to 4 March 2020. Articles were eligible if they described an oncology medical education initiative (with a clear intervention and outcome) within an LMIC. Articles were classified based on the target population, the level of medical education, degree of collaboration with another institution and if there was an e-learning component to the intervention. FINDINGS: Of the 806 articles screened, 25 met criteria and were eligible for analysis. The majority of initiatives were targeted towards physicians and focused on continuing medical education. Almost all the initiatives were done in partnership with a collaborating organisation from a high-income country. Only one article described the impact of the initiative on patient outcomes. Less than half of the initiatives involved e-learning. CONCLUSIONS: There is a paucity of oncology training and education initiatives in LMICs published in English. Initiatives for non-physicians, efforts to foster collaboration within and between LMICs, knowledge sharing initiatives and studies that measure the impact of these initiatives on developing an effective workforce are highly recommended.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".