Are community health nurse ready for e-learning?
Bibliographic record
Abstract
Geographical constrain has becoming a concern to nurses working in the community health setting as it hinder nurses from participating in continuing professional education(CPE) programme. One way of overcome this obstacle is provision of distance learning via on line learning. The aim of this study was to explore the community health nurses(CHN) readiness of e-learning as a means to CPE. A cross-sectional survey was carried out on 400 nurses to determine the readiness of nurses towards e-learning. Sample was randomly selected from public health clinics in four districts. Returns mail questionnaires were used to collect data. The data was analyzed using SPSS version 22. Only 75% (N=300) of the participants had returned the completed questionnaire. Almost all the participants (289, 96%) had experienced in using computer. However only 215 (71%) of them have internet connection at home. only three quarter of them use computer more than 2 hours per week. The most common usage was browsing internet information and writing report (156, 52.7%). All of them have no experience in elearning. The CHN showed high acceptance to e-learning with mean score of 4.5. they rated consider elearning were achieving life long learning (mean=4.1), flexibility in time and space (mean= 4.07) and broaden one's horizon with diversity and latest knowledge (mean = 4.07) as most encouraging factors that motivate them to participate in e-learning, however factors like limited time (mean =2.42), lack of support from supervisor and limited understanding about network system: (LAN, internet and intranet) (mean =3.00) may deter them from e-learning . Conclusion, finding from this study provide valuable insight to the nursing authority to consider e-learning to develop CPE programme to CHN who are at disadvantage of accessing face to face programme.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".