Size matters: what influences medical students’ choice of study site?
Bibliographic record
Abstract
BACKGROUND: The University of British Columbia, Canada doubled its class intake in 2004, creating, in addition to its main metropolitan campus, 2 distributed campuses, one in a medium sized island city, and the other in a small geographically isolated northern city. Our admission process attempts to identify students more suitable for education in our northern, rural site. Students also indicate their preferred site. Little is known about what influences student choice when they have more than one campus to choose from at a single medical school. AIM: To understand what influences students' preference of study site in a single medical school with 3 separate campuses, one with a rural mission. METHODS: We used qualitative methodology to examine what influenced student choice of study site. Semi-structured interviews were conducted with students at all three sites (n = 37). Iterative and independent coding and analysis took place to corroborate research findings. RESULTS: The primary theme was size of class and community. Some students viewed a larger class size and larger study and practice community as advantages, others viewed a smaller class size and smaller study and practice community as important. Additional themes were perceptions of quality of education, relationships, and lifestyle. These were related to the larger theme of class and community size and overlapped. Students articulated advantages and disadvantages of each site, and dynamic tensions in their choice of sites. Close relationships and educational experiences were emphasized at the smaller regional sites. Greater access to medical and educational specialists and the diversity offered by a larger, more anonymous class, patient case-mix, and community were emphasized at the originating and largest site. Partner and family--trumps--could overrule preferred site choices. CONCLUSION: Early and comprehensive descriptions of the differences between sites for students and their partners is needed to help truly informed choices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.159 | 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 teacher head, 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".