Design and Implementation of a Distant-Learning Clinical Research Mentorship Program: The Accra-Toronto Collaboration
Bibliographic record
Abstract
PURPOSE: For many oncology training programs in low- and middle-income countries, dedicated time for research education and mentorship of trainees is limited. Here, we report a 1-year-long collaboration between a cancer center in Canada and one in Ghana with the aim of imparting clinical research skills and mentoring the research of radiation oncology residents. METHODS: On the basis of a needs assessment conducted in Ghana, we designed a curriculum consisting of 13 weekly seminars delivered via videoconference, followed by a 1-year-long mentorship program to support research projects. The primary outcome was the feasibility of the program from seminars to manuscript preparation. We used multiple secondary outcomes to capture the learning experience with study-specific questionnaires. We evaluated critical thinking ability using the Berlin questionnaire. Funding was made available for research and travel to international conferences. RESULTS: Five Ghanaian trainees submitted research proposals. Nine Canadian faculty members delivered the seminars and two served as methodology mentors, and two Ghanaian faculty acted as local supervisors. Feedback questionnaires from all participants showed that they agreed strongly that they would recommend the sessions to another resident (75%), that the objectives were clear (71%), and that the topics were useful for their training (73%). At the end of the program, two Ghanaian trainees finalized their manuscripts and one was published. CONCLUSION: Here, we report on the implementation of a mentorship program focused on research methods and evidence-based medicine in sub-Saharan Africa. The program was successful in the drafting and publication of abstracts and manuscripts by local trainees.
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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 | 0.033 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".