Transforming the health landscape in northern communities: Shared leadership for innovation in nursing education
Bibliographic record
Abstract
People living in northern areas throughout the world experience poorer health status than their southern neighbours. Accessibility to health care services and availability of health care professionals play a role in the building of health capacity in northern regions. The College of Nursing at the University of Saskatchewan developed a principled approach to the creation of an indigenous nursing workforce in Northern Saskatchewan. This approach builds on Williams’ concept of Therapeutic Landscapes, which recognizes the connectedness among environment, social interaction, and symbolic meaning within a population, and offers a way to analyze the influence of the contextual factors of place on health, and values and attitudes on well-being. In order to succeed, the College developed mutually beneficial, capacity-building relationships with northern communities, finding local champions to assist them. They reorganized their administrative structure to give visibility to their northern relationships, and built a distributive learning approach based on the commitment to “learn where you live”. Measuring the success of such approaches requires the development of new and innovative evaluation strategies, beyond the usual markers of individual student success. It requires approaches that capture the impact of such education programming on the fabric of the community as a whole.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.004 |
| 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".