Use of clinical placements as a means of recruiting health care students to underserviced areas in Southeastern Ontario: Part 1 – Student perspectives
Bibliographic record
Abstract
OBJECTIVE: This two-part study examines the present gap between financial and educational incentives required and the recruitment strategies used to draw health science students to underserviced areas in Southeastern Ontario. Part 1 explores the impact of offering travel stipends, rent-free accommodation and interprofessional educational opportunities to health science students on their willingness to participate in clinical placements in underserviced areas. DESIGN: Mixed-method two-part study using a self-administered questionnaire. SETTING: Canadian university campus. PARTICIPANTS: Four hundred and sixty-eight senior level medical, nursing, occupational therapy, physical therapy and X-ray technology students from a Canadian university and affiliated professional school. MAIN OUTCOME MEASURES: The influence of currently established incentives on student willingness to complete a clinical placement in designated underserviced communities in Southeastern Ontario. RESULTS: Based on a 75% response rate, the results demonstrate that, in general, students agree that they are more willing to complete a clinical placement in an underserviced community if provided travel stipends (75%), rent-free housing (92%) and interprofessional educational opportunities (65%). Students also identified 15 additional factors influencing willingness. CONCLUSIONS: Students are more willing to complete clinical placements in underserviced communities if provided incentives. The findings of this study support an interprofessional clinical education and recruitment enhancement program in Southeastern Ontario.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".