Experiences of Northern Ontario Nurse Educators in Adapting Teaching Methods
Bibliographic record
Abstract
Nursing education is evolving, requiring educators to adapt teaching methods due to advances in technology, pedagogy, and accreditation standards. In Northern Ontario, geographic and resource limitations make this adaptability critical for ensuring equitable, high-quality education. Guided by Lewin’s change theory, this qualitative phenomenological study examined how Bachelor of Science in Nursing (BScN) nursing educators in Northern Ontario adapted their teaching practices to these evolving demands. Six full-time educators teaching in BScN programs with at least 3 years of recent nursing education experience using synchronous instructional methods participated in semistructured qualitative interviews. Data were analyzed using Saldaña’s coding framework. Four themes emerged from the analysis: recognizing the need to change methods, implementing active and student-centered approaches, adopting contemporary student-focused strategies, and embracing lifelong learning and reflective growth. Despite barriers such as underfunding and limited professional development, participants demonstrated resilience and innovation through collaborative, low-cost practices that enhanced student engagement and fostered professional renewal. The implications for positive social change include the potential for nursing educators to apply innovative, student-centered practices to improve outcomes, expand equitable access, and foster professional growth. Findings may also guide regionally responsive policy development to support sustainable nursing education in Northern Ontario and other underresourced contexts.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".