Perceptions of the Impact of Online Learning as a Distance-based Learning Model on the Professional Practices of Working Nurses in Northern Ontario | Perceptions de l’impact de l’apprentissage en ligne comme modèle d’apprentissage à distance sur les pratiques professionnelles du personnel infirmier du nord de l’Ontario
Bibliographic record
Abstract
Nurses in Canada face diverse challenges to their ongoing educational pursuits. As a result, they have been early adopters of courses and programs based on distance education principles and, in particular, online learning models. In the study described in this paper, nurses studying at two northern universities, in programs involving online learning, were interviewed about their learning experiences and the impact of these experiences on their practice. The study led to insights into the factors affecting teaching and learning in distance settings; the complex work-life-study roles experienced by some nurses; life and work realities in northern settings; and the sustained importance of access enabled by online learning approaches. Au Canada, le personnel infirmier fait face à divers défis relatifs à l’éducation permanente. Les infirmiers et infirmières ont donc été parmi les premiers à adopter les cours et programmes appuyant les principes de l’éducation à distance et, en particulier, les modèles d’apprentissage en ligne. Dans l’étude que décrit cet article, le personnel infirmier étudiant dans deux universités du nord de l’Ontario, dans des programmes utilisant l’apprentissage en ligne, a été interviewé au sujet de ses expériences d’apprentissage et de l’incidence que celui-ci a eu sur la pratique des soins infirmiers. L’étude a permis de mieux comprendre les facteurs qui affectent l’enseignement et l’apprentissage à distance, les rôles complexes que jouent certains infirmiers et infirmières dans leur travail-vie-formation, les réalités de la vie et du travail dans les contextes nordiques et l’importance durable de l’accès que permettent les approches d’enseignement en ligne.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".