“Balancing two worlds”: a constructivist grounded theory exploring distributed/decentralised nursing education in rural and remote areas in Canada and Norway
Bibliographic record
Abstract
A challenge confronting northern nursing is delivery of equitable and culturally competent nursing education. Advances in technology support distributed approaches for decentralised learning and enhance the feasibility of nursing education in rural and remote regions. However, there is limited scholarship on distributed/decentralised technologies in nursing education, particularly in northern and circumpolar regions. The purpose of this constructivist grounded theory research was to develop an enhanced understanding of the unique experiences of students, faculty and administrators who use distributed/decentralised methods and technology. Open-ended interviews were completed in 2015–17 with nursing students (n = 8), faculty and administrators (n = 6) at two universities using distributed/decentralised educational strategies in northern and circumpolar regions. Interviews, journal entries, field notes and memos, were analysed using grounded theory procedures. Findings indicated that distributed/decentralised programs offered rural and remote students educational possibilities that “fit” which would not have otherwise existed. However, Balancing Two Worlds created a collision of roles resulting in the potential loss of balance. Students rectified the Fear of “Falling Off” of their program through four subprocesses: Being Disciplined, Having Realistic Expectations, Planning Ahead and Staying Motivated which provided structure and predictability. Findings support the development of empirical knowledge regarding distributed/decentralised technologies in nursing education and a foundation for future research.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".