Linking Learners for Life Where They Live (L4): Developing a Global Health Initiative for Student Engagement
Bibliographic record
Abstract
This article describes a graduate student learning experience as part of an international nursing collaborative working together to develop an academic partnership for global health education in the circumpolar north. The experience provided an opportunity to conduct a pilot project in a rural, remote, northern community using an indigenous, global context. Building on the Canadian-Siberian collaboration, the graduate student attended an academic institution in Siberia, where she focused on the sharing of expertise, knowledge, and insights in order to address the challenges facing indigenous people in achieving optimal health and well-being in the circumpolar north. The goal was to create a foundation for "putting health into place" in a northern context, with the hope of creating shared learning opportunities for undergraduate students between the 2 countries.The intent is to share the approach used by the graduate student to use a conceptual model to assess the feasibility of creating a context-relevant global health experience for northern nursing education.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".