Influences on medical studentsâ clinical school preferences: outcomes from a Rural Clinical School immersion program in Australia
Bibliographic record
Abstract
CONTEXT: In Australia, rural clinical schools (RCSs) were developed to address the maldistribution of the rural medical workforce. Evidence demonstrates that medical students who attend an RCS, or have a rural background, are more likely to become rural doctors. To enhance the likelihood of our graduates from Deakin University becoming rural doctors, we strategically combined these two independent factors and created a dedicated rural training stream (RTS), which commenced in 2022. To support the introduction of the RTS and provide students with an authentic RCS experience, we developed a 3-day RCS immersion program for year 1 students. The broad aim was to provide students with experience and knowledge that would allow them to make an informed clinical school preferencing decision. Despite delivering the same curriculum, each of Deakin University's three RCS campuses are shaped by their distinct clinical setting, community and approach to program delivery. To showcase these individual aspects, each RCS designed a bespoke 3-day immersion program centred around three themes: connecting students to the local Indigenous Country, the community and the clinical school. ISSUE: Historically, our students' clinical school preferences have fluctuated annually, with the majority of students generally electing to remain at the years 1 and 2 urban training location. This phenomenon was unsurprising as the majority of students, with metropolitan backgrounds, had little understanding of what living and learning in a rural community would be like. Clinical school promotional activities, before the introduction of the RTS, were held at the preclinical urban campus. Only a small number of students would visit one or more of Deakin University's five clinical schools on an ad-hoc basis. The available research on how medical students make their clinical school preferencing decisions highlights that both personal and learning needs are considerations. However, we lacked evidence on factors influencing our own students' clinical school decisions. Information provided to prospective students focused solely on the clinical schools, with an absence of practical information about the rural community or Country. The introduction of the RTS and immersion program provided an opportunity to explore medical students' decisions when preferencing clinical schools, offering learnings to enhance the associated policies and procedures. LESSONS LEARNED: The program achieved its overarching aim of providing students with realistic exposure to the RCS environment, with 86.9% agreeing the experience helped them to make informed decisions about their clinical school preferences. The program, initially a pilot, has become embedded in the year 1 curriculum. Participation in the immersion program reduced student hesitancy towards attending an RCS, with over a quarter of initially hesitant students ultimately ranking an RCS as their first preference. Furthermore, there was a significant positive shift in students indicating that they were confident that the RCS would be the best environment for them (p=0.001). The linking of immersion evaluation data and clinical school preference information provided insights into students' perceptions of the program, the RCSs and the factors influencing their preferences. When viewed collectively, it was evident that a review of our clinical school allocation process was warranted. This will be monitored as our RTS develops, particularly with the introduction of preclinical learning campuses (2024) in two prominent rural locations.
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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 | 0.004 | 0.017 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".