Training of oncologists: Results of a global survey.
Bibliographic record
Abstract
10526 Background: While several studies have highlighted the global shortages of oncologists and their workload, few have studied the characteristics of current oncology training. Methods: An online survey was distributed through a snowball method via national oncology societies and a pre-existing network of contacts to cancer care providing physicians in 57 countries. Countries were classified into low- or lower-middle-income countries (LMICs), upper-middle-income countries (UMICs), and high-income countries (HICs) based on World Bank criteria. Results: 273 physicians who trained in 57 different countries responded to the survey; 33% (90/273), 32% (87/273), and 35% (96/273) in LMICs, UMICs and HICs respectively. 60% of respondents were practicing physicians and 40% were in training. The proportion of trainees was higher in LMICs (51%; 45/89) and UMICs (42%; 37/84), than HICs (19%; 28/96; P = 0.013). A higher proportion of respondents from LMICs (37%; 27/73) self-fund their core oncology training compared to UMICs (13%; 10/77) and HICs (11%; 10/89; P < 0.001). Respondents from HICs were more likely to complete an accepted abstract, poster and publication from their research activities compared to respondents from UMICs and LMICs (abstract: 37/72 (51%) from HICs, 18/66 (27%) from UMICs, 24/65 (37%) from LMICs, P = 0.014; poster: (42/72 (58%) from HICs, 28/66 (42%) from UMICs, 13/65 (20%) from LMICs, P < 0.001; publication: 43/72 (60%) from HICs, 32/66 (49%) from UMICs, 24/65 (37%) from LMICs, P = 0.029). Respondents identified several barriers to effective training including skewed service to education ratio and burnout. With regards to preparedness for practice, mean scores on a 5-point Likert scale were low for professional tasks like supervision and mentoring of trainees, leadership and effective management of an oncology practice, and understanding of healthcare systems irrespective of country grouping. Conclusions: Investment in training by the public sector would be vital to decreasing the prevalence of self-funding in LMIC. Gaps in research training and enhancement of competencies in research dissemination in LMIC require attention. Instruction on cancer care systems and leadership need to be incorporated in training curricula in both LMICs and HICs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".