Becoming a web‐based learner: registered nurses’ experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: The purpose of the study was to describe Registered Nurses' experiences when taking a web-based course from either the workplace or home, and the impact of their learning on clinical practice. RATIONALE: Little is known about the web-based learners' experience, particularly when courses are accessed from the nursing practice setting. Even less is known about whether nurses transfer their web-based learning to clinical practice. METHODS: A qualitative design employing focus group interviews was used. Participants included hospital and community nurses from three Canadian provinces and one territory. Data were collected at three points over a 6-month period and analysed using a thematic analysis process. These findings emanate from a larger study using survey method and focus group interviews. RESULTS: The focus group interviews captured the hurdles nurses faced during the first weeks when they struggled with technology, re-framed their views of teaching and adjusted to web-based learning from home and work. These first stressful weeks were followed by a period during which nurses developed relationships with the teacher and peers that enabled them to focus on learning and prevented attrition. Most nurses reported the web course was convenient and that they would be interested and comfortable using technology for learning and work purposes in the future. Six weeks after the course was completed, nurses articulated a number of ways the course had improved their practice. CONCLUSION: Initial weeks in a web-based course can be very challenging for novice Internet users, however, most nurses who completed the course reported a positive learning experience. Nurses, employers and educators should evaluate computer skills, computer access and the learning environment when preparing for web-based learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it