Increasing medical student interest in general practice in New Zealand: where to from here?
Bibliographic record
Abstract
INTRODUCTION: To meet increasing health demands, increasing the proportion of local graduates entering general practice is imperative. METHODS: Students entering or exiting The University of Auckland's medical programme from 2006 to 2008 were invited to complete a tracking project survey. Levels of interest in general practice were determined along with characteristics associated with a greater or lesser interest in this career. RESULTS: 712 students replied--a response rate of 80%. At entry, 40% of students had a strong interest in a career in general practice, and at exit, 29% (P =0.003). A quarter at each time point had no interest. The proportion of domestic students born outside NZ or Australia was 160/376 (42.5%). There were significantly higher levels of interest in general practice among females, students born in NZ, and those from outside Auckland--especially rural origin. Flexibility in career was more important to students with a strong interest in general practice than those with no interest. DISCUSSION: Auckland medical students have levels of interest in general practice comparable with international data. Increasing this interest further may require admission of a greater proportion of students from those groups with higher interest levels, greater emphasis on the positive aspects of general practice, and on GPs as equals to other specialists.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".