Bibliographic record
Abstract
Context and setting A Canadian province, 4 times the size of the UK, has only 1 medical school. The northern and rural areas of the province are underserved medically, with difficulties in doctor recruitment and retention. In 2004, the medical school opened a northern campus for undergraduate medical education in partnership with a northern university. The programme's mission is to admit and train future doctors who are more likely to locate their clinical practice in northern and rural settings, requiring changes to the admissions process. Why the idea was necessary The standard admissions process for the main school is based 50% on cognitive and 50% on non-cognitive criteria, but no assessment is made on the student's background. Other schools have looked at geographical origin, race or the results of personality testing. However, we found no existing admissions process to meet the needs of this programme, namely to admit students who fit well in a northern programme site. What was done The partners developed an admissions tool, the Rural and Remote Suitability Score (RRSS) to evaluate applicants' suitability for education in the north. Predictors of eventual rural or northern practice location were developed by a literature review and focus groups, resulting in the development of admission criteria and a marking scheme. The instrument was piloted with northern students currently studying medicine, students in urban settings, and postgraduate trainees who had chosen a northern site for further training. Only 1 question was added to the admissions form, which already included an autobiographical essay and documentation of non-academic activities. Using this submitted material, the tool develops a score in 3 domains (northern background or experience, self-reliance, and recreational preferences) to develop an overall RRSS score out of 100. For example, experience of working in a rural community, participation in typical, rural outdoor activities such as fishing and hunting, and travelling independently or working in jobs requiring independent decision making would all score specific points in the RRSS score. The instrument was used to score applications in 2004 (n = 1308); interrater reliability alpha was 0·90, and RRSS scores were used in admissions decisions to the northern programme. Evaluation of results and impact Review of the applicant RRSS scores ensured an adequate pool of potential candidates for interview for the northern programme. Weighted use of the RRSS score in admissions decisions ensured that selected applicants were likely to fit well into this educational environment. In comparison to grade point average, interview scores and other admission criteria, the RRSS was the single significant predictor of the applicant's first choice of education location. All but 1 student selected for the northern programme had placed the northern programme in the top 2 out of 3 possible options. Most importantly, 25 students are happily studying on a northern campus, embraced by a community that feels they are the ‘right’ students for their site and programme.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 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; both teacher heads agree on what is shown here.
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".