Should I apply to medical school? High school students and barriers to application.
Bibliographic record
Abstract
INTRODUCTION: A major goal of the Faculty of Medicine at the Memorial University of Newfoundland is to produce physicians who will return to rural areas that are currently underserviced. Research shows that the strongest indicator of practice in a rural area is a rural background, and thus it is important that rural students apply to medical school. We investigated what high school students believe to be preventing them from pursuing medical education. METHODS: Between September 2013 and June 2014, we administered a paper survey to high school students in Newfoundland and Labrador, New Brunswick and Prince Edward Island. RESULTS: A total of 665 participants completed the survey. We found that fewer rural students (75.6%) than urban students (98.6%) believed that they could gain admission to medical school (p < 0.01) and that medicine was promoted as a career choice in fewer rural schools (55.7%) than urban schools (69.7%). Also, 55.4% of urban students, but only 44.4% of rural students, believed that rural students were disadvantaged when applying to medical school. CONCLUSION: In our study, rural students believed they were less likely to be accepted into medical school than urban students, and fewer rural students felt that medicine was promoted as a potential career choice. Our results may be explained by a lack of role models or perhaps by financial barriers, although further research is needed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".