Learn Where You Live, Teach From a Distance: Choosing the Best Technology for Distributed Nursing Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it