Learn Where You Live, Teach From a Distance: Choosing the Best Technology for Distributed Nursing Education
Bibliographic record
Abstract
Rural and remote communities within the Circumpolar World have been challenged to provide on-site opportunities for post-secondary education due to geographical barriers and a lack of available resources. Distributed learning is defined as the separation of time and/or space in teaching and learning and therefore offers possibilities that can be tailored for programs, faculty, and individual students. Distributed learning not only mitigates geographical and resource challenges but, most importantly, it provides learning experiences that are context relevant. The intent of this report is to illustrate how one western Canadian nursing education program has moved beyond traditional methods of educational distance delivery to include a more learner-centred approach. The ”learn where you live” program was developed to provide accessible, quality undergraduate nursing education to northern rural and remote communities. This novel educational approach supports the educator to be in two places at one time in a synchronous, face-to-face delivery in which students are taught from a distance rather than having to relocate. This approach to nursing education is based on the premise that it is the educator and not the student who is remotely situated. The authors advise that there is no normative preference for a particular type of technology. Best practices are evolving through circumpolar collaborative partnerships in northern nursing education. This report is part of a special collection from members of the University of the Arctic Thematic Network on Northern Nursing Education. The collection explores models of decentralized and distributed university-level nursing education across 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".