Use of clinical placements as a means of recruiting health care professionals to underserviced areas in Southeastern Ontario: Part 2 – Community perspectives
Bibliographic record
Abstract
OBJECTIVE: Part 2 of this two-part study identifies current recruitment strategies and existing incentives used by underserviced communities to recruit health science students during the clinical placement stage. Discussion surrounding current gaps in recruitment strategies and potential funding sources are explored. DESIGN: Mixed-method two-part study using a self-administered questionnaire. SETTINGS: Six community hospitals and one private practice. PARTICIPANTS: Community resource contact from seven underserviced communities in Southeastern Ontario. MAIN OUTCOME MEASURES: Level of community agreement that current recruitment strategies include travel stipends, rent-free accommodation and interprofessional education opportunities. RESULTS: A 100% response rate established that one sample community provides travel stipends, three provide rent-free accommodation, and four offer interprofessional education opportunities. These incentives were frequently offered exclusively to medical students. CONCLUSIONS: When considering the results from part 1 of the study, there is a substantial gap between financial incentives students deem important in the creation of an appealing clinical placement opportunity and the provisions offered to them by the sample communities. The findings of this study support the need for a 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".