Rural Mentorships in Health Care: Factors Influencing Their Development and Sustainability
Bibliographic record
Abstract
BACKGROUND: The need to recruit and retain health care providers remains a concern in rural communities. This project aimed to better understand what health care providers and senior leaders value in mentorship and determine the best way to implement a mentorship program in rural western Canada. METHOD: Health care providers and senior leaders from a rural health region were recruited through convenience and snowball sampling. Participants were interviewed using a semistructured interview guide, and data were analyzed using thematic analysis. RESULTS: Two main themes were revealed: rural mentorship challenges and facilitators. Challenges included administrative, scope of practice, and interpersonal, whereas facilitators included mentorship as a recruitment and retention strategy, openness and commitment, structured mentorship programs, and community influence. CONCLUSION: This information will enable administrators and educators to more successfully implement mentorship programs for a variety of health care professionals working within rural environments and facilitate staff development, recruitment, and retention. J Contin Educ Nurs. 2018;49(7):322-328.
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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 | 0.020 | 0.079 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".