Internationally educated nurses experiences of rural nursing practice in Western Canada
Bibliographic record
Abstract
Objective: Internationally Educated Nurses (IENs) are an important component of the healthcare workforce and a contributing solution to the nursing shortage as well as healthcare sustainability in rural and remote areas. The study’s aim is to contribute to the discussion regarding IENs’ experiences of rural nursing practice to inform practices, procedures, activities, and policies. Methods: As part of a sequential mixed methods study, individual interviews and a photovoice approach, was used to describe IENs’ experiences in nursing practice in rural communities in Alberta, Canada. This paper reports the findings from the semi-structured interviews. Results: The qualitative findings revealed two themes: (1) Thriving in Rural Nursing Practice and (2) Rurality. The theme of Thriving in Rural Nursing Practice consists of two broad categories: Wider scope of practice and Becoming part of the team. The theme Rurality includes the categories of Challenges of relocating to a rural community and Integrating into the community. Conclusions: This study highlights practical and unique activities and strategies to enhance the experiences of IENs in rural nursing practice. IENs need to navigate their work environment by utilizing strategies to adjust to a wider scope of practice (e.g., competencies, self-learning, “lifelines”, and “IEN work buddies”). Managers, nursing colleagues, and IENs together must facilitate becoming part of the team and addressing the monocultural workplace via mentorship, supportive relationships, acceptance of cultural differences, and recognition of IENs’ knowledge and skills. Practical issues, such as housing, connection with community members, and participation in community activities, require careful attention to address relocation challenges and community integration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".