Facilitators, Barriers, and Educational Preparedness of Early-Career Nursing Graduates Entering Practice in Rural and Remote Areas: A Mixed-Method Study
Bibliographic record
Abstract
Background/Objective: A nurse staffing crisis is a high-profile issue in the healthcare system. The challenge accelerates when considering the status of the nursing workforce in rural and remote (R&R) areas, where recruitment and retention are mounting problems. The primary focus of this study was to evaluate facilitators and barriers to entry into R&R nursing practice, alongside understanding educational preparedness to practice in these settings in Manitoba. Methods: A sequential explanatory mixed-methods survey and qualitative interviews were used as a study design to explore this emerging problem. Study participants include registered nurses (RNs) and Licensed Practical Nurses (LPNs) practicing in Manitoba’s R&R areas within three years of graduation from a nursing program. Results: A total of 77 nurses (56-RNs and 21-LPNs) participated in the survey, while 16 nurses were interviewed subsequently. Having a positive workplace culture (70%), being born or residing in an R&R area before practicing as a nurse (66%), and having a good clinical variety of patients (65%) were identified as key facilitators. Unmanageable workload with inadequate staffing (50%) and inadequate resources and infrastructure (46%) were identified as key barriers to entering R&R nursing practice in Manitoba. Through qualitative interpretive descriptions, the generalist role, autonomy, rural life, and organizational culture were identified as facilitators, while resources, staffing, geography, and expanded roles were identified as barriers. Conclusions: Preparing new nursing graduates for the realities they face in R&R areas is paramount. The current study findings help inform R&R curriculum in undergraduate nursing programs and consider strategies to enhance employment opportunities for new nurses in these dynamic settings.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".