Oncology training programmes for general practitioners: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Due to the increasing global burden of cancer and the shortage of trained medical oncologists, training General Practitioners (GPs) in Oncology (known as GPOs) has been proposed as a means to potentially ease some burden on medical oncologists with heavy workloads, especially in low-and-middle-income countries (LMICs), by task-sharing and task-shifting. We undertook a scoping review to identify and characterise the existing training programmes and curricula for GPOs globally. DESIGN: We searched three major electronic databases: EMBASE, Medline/PubMed and Education Source for articles that described a medical oncology training programme for GPs. All study types were eligible in this review. We followed a two-stage standardised screening process using two independent reviewers to evaluate the eligibility of the articles. RESULTS: = 2), a short, 1.5-day workshop and a 10-hour course. In the grey literature, GPO training programme durations ranged from 2 weeks to 13 months. A mixture of delivery methods was employed including didactic lectures and clinical rotations. CONCLUSION: This scoping review identified a small number of heterogeneous studies and grey literature sources that described and/or evaluated medical oncology training programmes for GPs. The information synthesised here can be used to foster the collaboration needed for the continued development of GPO programmes that could help address the problem of lack of workforce to meet the rising burden of cancer, especially 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 | 0.023 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".