Oncology training and education initiatives in low and middle income countries: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it