Northeastern Ontario registered nurses’ perceptions of E-learning
Bibliographic record
Abstract
Nurses face challenges in meeting their continuing education needs. E-learning can help \nto overcome these barriers and has become a common method of nursing continuing education. \nAn integrative literature review found that e-learning benefits nurses’ learning, patient care, and \nthe hospital organization in different international contexts. E-learning has unique barriers, areas \nfor improvement, and additional research is needed in the Northeastern Ontario context. \nAn interpretive description qualitative study explored the perceptions of ten Northeastern \nOntario registered nurses about e-learning. The aim was to understand this phenomenon and \ninform continuing education practices. Bridging the Gap was the overarching theme, providing a \nrich description of how nurses perceived e-learning as a continuing educational method. The \noverarching theme was supported by the subthemes: Unsatisfactory Learning Experiences, \nMeaningful Learning Experiences, and Enhancing Learning Experiences. These findings could \ninform future e-learning continuing education and support patient care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".