Motivating Factors of and Perceived Barriers to Research at a Canadian Medical University with Regional Campuses: A Cross-Sectional Survey Study
Bibliographic record
Abstract
Introduction Research training programs are an integral part of a well-rounded medical education. These programs help students contribute to medical knowledge, develop skills in critical evaluation and research dissemination, and they facilitate the training of our future medical researchers. Existing literature suggests barriers including lack of available time and access to projects may hinder a medical student’s research training. These barriers likely differ based on the medical school curriculum, and there exists limited data looking at these attitudes in Canadian students, students outside large academic centers or those in condensed programs. Given this, our study aims to further explore medical students' perceptions, perceived barriers of research in a three-year Canadian undergraduate medical school with regional campuses. Methods We conducted a cross-sectional survey of medical students (classes of 2019, 2020 and 2021) representing the main and regional campuses of the Michael G. DeGroote School of Medicine in Ontario, Canada to explore their attitudes towards research. Descriptive statistics were used to describe participant demographics, research background and attitudes towards research. Results Overall, 70.4% of students identified as being involved in a research project at some point during their medical school tenure. Motivating factors for research participation included the goal of obtaining a residency spot (63.0%), and interest in their research topic (74.7%). Barriers to research included perceived lack of available time (31.5%), and difficulty in finding a research project (44.5%). Perceived curriculum deficiencies included lack of education in research methodology and appraisal of scientific literature (93.2% and 89.0%, respectively). Lastly, regional campuses tended conduct their research outside of their home campus (43.6% vs 3.3%, p<0.0001). Conclusion The findings from our study highlighted the students’ attitudes towards research in a Canadian medical school that has multiple campuses and a shortened medical curriculum (3-year). It identifies potential areas of improvement from a student perspective, which can hopefully be utilized by medical educators to continue the improvement of medical trainee research training.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".