Supporting nurses' transition to rural healthcare environments through mentorship
Bibliographic record
Abstract
INTRODUCTION: The global shortage of rural healthcare professionals threatens the access these communities have to adequate healthcare resources. Barriers to recruitment and retention of nurses in rural facilities include limited resources, professional development opportunities, and interpersonal ties to the area. Mentorship programs have been used to successfully recruit and retain rural nurses. This study aimed to explore (i) employee perceptions of mentorship in rural healthcare organizations, (ii) the processes involved in creating mentoring relationships in rural healthcare organizations, and (iii) the organizational features supporting and inhibiting mentorship in rural healthcare organizations. This study was conducted in one rural health region in Saskatchewan, Canada. METHODS: Volunteer participants who were employed at one rural healthcare facility were interviewed. A semi-structured interview guide that focused on exploring and gaining an understanding of participants' perceptions of mentorship in rural communities was employed. Data were analyzed using interpretive description methodology, which places high value on participants' subjective perspective and knowledge of their experience. RESULTS: All seven participants were female and employed as registered nurses or licensed practical nurses. Participants recognized that the rural environment offered unique challenges and opportunities for the transition of nurses new to rural healthcare. Participants believed mentorships facilitated this transition and were vital to the personal and professional success of new employees. Specifically, their insights indicated that this transition was influenced by three factors: rural community influences, organizational influences, and mentorship program influences. Facilitators for mentorships hinged on the close working relationships that facilitated the development of trust. Barriers to mentorship included low staff numbers, limited selection of volunteer mentors, and lack of mentorship education. CONCLUSIONS: The rural community context clearly presents challenges for the transition of nurses. Participants described mentorship as a vital component to personal and professional success of new employees in rural areas. The findings of this qualitative exploratory study inform the development of creative and supportive ways to establish mentorships to address the challenges specifically associated with integration of nurses into rural practice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".