Blending learning: The preferred choice of clinical nurse educators to provide continuing professional development
Bibliographic record
Abstract
Introduction and objective: A clinical nurse educators’ (CNE) work is primarily focused on ensuring that fellow registered nurses have the skills and training to improve their clinical practice and maintain their professional competence. In recent years, resource limitations and a growing emphasis on self-directed learning have increased the pressure on nurse-educators to integrate e-learning into their teaching methods. While research has evaluated the experiences of nurses on this topic, limited understanding is known of CNEs’ experiences. Purpose: This qualitative study explored the CNEs’ experiences in facilitating continuing professional development for their nurses and the integration of e-learning in a University Health Center in Quebec, Canada.Methods: The sample consisted of 7 CNEs, who had more than one to 15 years of experience in their current position. Their experiences with e-learning varied: it ranged from incorporating a video-clip in their presentations, to providing input into the learning management system they tested. Semi-structured interviews were thematically analyzed. Results: Despite participants varied levels of knowledge towards e-learning, all were convinced that this method could be used complementarily alongside hands-on training. Though they recognized the importance of human contact in teaching, they also understood the limitations of the traditional pedagogy; lacking the addition of interactive features. Despite some criticism, CNEs were able to identify opportunities where e-learning could be useful: during nursing orientation, tracking, evaluation and accreditation purposes, content refreshment, and to standardize protocols.Discussion and conclusions: More research is needed, and cooperative efforts are required from nurses and nurse-management to engage in the promotion of professional development.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".