Clinical research mentorship programme (CRMP) for radiation oncology residents in Africa—building capacity through mentoring
Bibliographic record
Abstract
Research skills are mandatory for all oncology residency training programmes.Creating the environment to foster skills and passion can be a challenge in all settings, and a unique challenge in low and middle income countries (LMICs).Tremendous clinical workload places exceptional demand on clinician teachers, research infrastructure and access to research collaborators with diverse methodological skill sets can be limited.International collaborations, and in particular relationship partnerships (Whitehead et al ((2018) Acad Med 93 1760-1763)) can be a useful approach to bridge resource gaps and enrich the support available to trainees (Research EoH ((2014) TDR/ESSENCE/2.14)).The Clinical Research Mentorship Programme (CRMP) is a collaborative initiative created by the University of Toronto Department of Radiation Oncology, Princess Margaret Cancer Centre, delivered in collaboration with LMIC radiation oncology residency programmes with the primary goal of enriching the research experience of LMIC oncology trainees.It was inspired by observing a need, an enthusiasm to collaborate and some seed funding that supported the idea.At the heart of the programme is a formalised relationship, a triad, between a LMIC oncology trainee, their local supervisor and a mentor from Toronto.Within the collaborative environment created between the LMIC and high income country (HIC) institutions, enabled by remote learning technologies, a 12-week research methods seminar kick starts a year-long mentorship for the trainee on their research question.The goal is to enrich the quality of the research experience for the trainee, resulting in dissemination of research findings in international conferences and publications.A standard evaluation package is used (Vuple et al ((2021) 6 919-928)).In this paper, through a description of our collaboration, we will highlight how a distant mentorship programme was used to enhance clinical research mentorship skills for radiation oncology trainees in Africa.We hope the format we have chosen will continue to demonstrate effectiveness for our trainees, sustainability for our faculty and institutions and will serve as one mechanism to build radiation capacity for LMIC through collaboration, mentorship and research.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".