A descriptive study of registered nurses' experiences with web‐based learning
Bibliographic record
Abstract
AIMS: To describe the experiences of registered nurses (RNs) who enrolled in a web-based course from either their home or the workplace. RATIONALE: In order to maintain competency in rapidly changing health care systems, and meet the challenge of overcoming traditional barriers to continuing education, RNs need access to innovative educational delivery methods. As yet, little is known about the web-based learners' experience, particularly when courses are accessed from the nursing practice setting. METHODS: The article focuses on the results from questionnaires conducted with 57 RNs enrolled in a web-based, postdiploma course. These findings emanate from a larger study using survey method and focus group interviews. Nurses' experiences were measured using the Online Learner Support Instrument which was developed and tested for use in the study. RESULTS: Most nurses found the course highly satisfactory. Not all experiences were positive however, and a number of challenges were faced. Access to the course from home was reported as very satisfactory for the majority, while work users encountered a number of serious barriers such as insufficient time and limited computer access. The RNs made significant gains in their learning with e-mail, Internet, keyboarding and word processing skills during the 16-week course. Lack of computer skills, erroneous perceptions of course workload and inadequate preparation for web learning were largely responsible for the majority of withdrawals. CONCLUSION: Web-based learning can be an effective mode of delivery for nursing education. Advance preparation by educational institutions, employers and prospective students is essential. Teachers, peers, technology, course design and the learning environment are key variables that influence the learners' experience and success.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".