Role of Non-Government Organizations in Engaging Medical Students in Research
Bibliographic record
Abstract
The continued decline in medical trainees entering the workforce as clinician-scientists has elevated the need to engage medical students in research. While past studies have shown early exposure to generate interest among medical students for research and academic careers, financial constraints have limited the number of such formal research training programs. In light of recent government budget cuts to support research training for medical students, non-government organizations (NGOs) may play a progressively larger role in supporting the development of clinician-scientists. Since 2005, the Mach-Gaensslen Foundation has sponsored 621 Canadian medical student research projects, which represents the largest longitudinal data set of Canadian medical students engaged in research. We present the results of the pre- and post-research studentship questionnaires, program evaluation survey and the 5-year and 10-year follow-up questionnaires of past recipients. This paper provides insight into the role of NGOs as stakeholders in the training of clinician-scientists and evaluates the impact of such programs on the attitudes and career trajectory of medical students. While the problem of too few physicians entering academic and research-oriented careers continues to grow, alternative-funding strategies from NGOs may prove to be an effective approach in developing and maintaining medical student interest in 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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".