Bibliographic record
Abstract
INTRODUCTION: For many rural Canadians nursing care is the primary and often the sole access point to health care. As such, rural nurses are an invaluable resource to the health and wellbeing of rural populations. However, due to a nursing workforce that is aging and retiring, limited resources and support, healthcare reform issues, and other factors, these rural professionals are in short supply. Because of limited opportunities to learn about rural practice settings, nursing students may be reluctant to select rural practice locations. Relevant and effective educational initiatives are needed to attract nursing students to underserved rural and remote communities so that rural people receive the health care they require. The purpose of this study was to explore the use of the innovative research approach called photovoice as an educational strategy to foster learning about and interest in rural locations and rural nursing as future practice settings. Fostering of interest in rural may help to address nursing workforce shortages in rural settings. METHODS: Thirty-eight third and fourth year nursing and health sciences students enrolled in an elective 'Rural Nursing' course used the qualitative research method photovoice to take photographs that represented challenges and facilitators of rural nursing practice. They then engaged in written reflection about their photos. Photos were to be taken in rural settings of their choice, thus fostering both urban and rural student exposure to diverse rural communities. RESULTS: One hundred forty-four photos and reflections were submitted, representing students' appreciation of diverse facilitators and challenges to rural nursing practice. Facilitators included technology, a generalist role, strong sense of community, and slower pace of life. Challenges included inadequate rural education in undergraduate nursing programs, professional isolation, safety issues, few opportunities for professional development, lack of anonymity, and insider/outsider status. Exemplar photos and reflections are provided. CONCLUSIONS: The photovoice research approach used in this rural education endeavour proved to be very useful in fostering students' exposure to, interest in, and understanding of rural settings and their influence on rural nursing practice. Photovoice is also recommended for use in rural courses other than nursing. Suggested strategies include group photovoice experience and the expansion of reflection to enhance rural health research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".