Mentorship in Rural Healthcare Organizations: Challenges and Opportunities
Bibliographic record
Abstract
Introduction: Recruitment to and retention of healthcare professionals in rural workplaces are often difficult due to inadequate resources, limited positions, and facility remoteness. Mentorship of employees can serve as a recruitment and retention strategy in rural organizations. Purpose: The purpose of this study was to explore managers’ perceptions of their roles in creating mentoring cultures, discover the processes in creating a culture of mentoring, and explore the organizational features supporting and inhibiting mentoring cultures. The objectives included: (a) exploring managers’ perceptions of their role in creating a mentoring culture, (b) discovering the processes of creating a culture of mentoring, and (c) exploring the organizational features supporting and inhibiting this process of developing a mentoring culture. The rural-specific findings from the larger study will be discussed.Sample: Twenty-seven front-line nurse managers working in acute care hospitals, long term care, and integrated facilities from both urban and rural locations were interviewed. Twelve participants managed rural facilities and their opinions surrounding rural mentorship strategies and challenges emerged.Method: Data was analyzed using Glaserian Grounded Theory.Findings: Nurse managers discussed the impact of being in a rural area and highlighted the rural-specific considerations for mentoring. They outlined the strategies, challenges, and opportunities for supporting staff mentoring relationships in rural organizations. Participants stated all employees were responsible for mentoring new individuals, regardless of occupation. Managers believed cross-professional mentoring enabled staff to understand team member roles and established collaborative work environments.Conclusions: In order to successfully recruit and retain healthcare employees in rural areas, innovative mentorship initiatives to ensure quality work environments are encouraged. Interprofessional mentorship can assist with the challenges of socializing new employees to rural workplaces by offering a means to encourage collaborative relationships and ultimately foster positive patient outcomes. Key Words: Mentorship, Rural Organizations, Interprofessional, Healthcare
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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 | 0.013 | 0.014 |
| 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 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".