A scoping review of oncology medical education interventions in low and middle income countries.
Bibliographic record
Abstract
e23004 Background: A large proportion of the global cancer burden occurs in low and middle income countries (LMICs). One of the significant barriers to adequate cancer control is the lack of an adequately trained oncology workforce. Medical education and training initiatives in oncology are necessary to tackle growing cancer incidence and mortality rates. We performed a scoping review of oncology medical education interventions in LMICs to understand the strategies used to train the global oncology workforce. Methods: We searched OVID MEDLINE and EMBASE databases between January 1, 1995 and March 4, 2020 using a standardized scoping review framework. Articles were eligible if they described an oncology medical education intervention within an LMIC with clear outcomes. Articles were classified based on the target population, the level of medical education, form of collaboration with another institution and if there was an e-learning component to the intervention. Results: Of the 806 articles screened, 25 met criteria and were eligible for analysis. The Middle East/Africa was the most common geographic area of the educational initiative (N=14/25). The majority of interventions were targeted towards physicians (n=15/25) and focused on continuing medical education (n=22/25). Twelve articles described the use of e-learning as part of the intervention. Twenty four articles described some form of collaboration, most commonly with an institution from a high-income country. Language barriers, technology, and lack of physical infrastructure and resources in the LMIC were the most common challenges described. The majority of the initiatives were funded through grants or charitable donations. Conclusions: There is a paucity of published interventions of oncology medical education initiatives in LMICs. Continued medical education initiatives and those targeted towards physicians are most common. There is a lack of collaboration between LMICs in these interventions. Further interventions are needed earlier during medical training and for non-physicians. In addition, increased use of e-learning interventions may overcome certain identified challenges. Encouragement of locally funded initiatives as well as scholarly evaluation and publication of these initiatives are important to improve cancer care in LMICs.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".