Under the Same Sky: Connecting Students and Cultures through Circumpolar Nursing Education
Bibliographic record
Abstract
The recruitment and retention of health professionals in rural, remote, and northern regions is an ongoing challenge. The Northern Nursing Education Network brought together nursing students working in rural and remote regions of the circumpolar north in Innovative Learning Institute on Circumpolar Health (ILICH) events to create opportunities for shared learning and expose both students and faculty to local and traditional knowledge that informs health behaviors specific to regions with Indigenous populations. Using participant experience data extracted from program discussions, evaluations, and reflective notes conducted after ILICH events held in 2015⁻2017, this paper explores how these two-week institutes can contribute to knowledge that is locally relevant yet transferable to rural areas across the circumpolar north. The findings clustered around experiences related to (1) Language as a barrier and an enabler; (2) shared values and traditions across borders; (3) differences and similarities in nursing practice; (4) new perspectives in nursing; and (5) building sustainable partnerships. Students learned more about their own culture as well as others by exploring the importance of language, cultures, and health inequity on different continents. Shared values and traditional knowledge impacted student perspectives of social determinants of health that are highly relevant for nurses working in the circumpolar north.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".