Nursing Students' Views on an e-Learning Activity About Health Promotion for Older Adults: A Cross-Sectional Study
Bibliographic record
Abstract
Purpose: Nurses are graduating ill-prepared to work with older adults across care contexts. The education nursing students receive about older adults often focuses on managing illnesses rather than promoting health. To expand the education that nursing students receive regarding health promotion and older adults, we examined nursing students' perceptions of an e-learning activity on health promotion with older adults. Method: We used a cross-sectional survey design. We included first-year baccalaureate nursing students ( N = 260) at a Canadian university. Students were required to complete the module, but only those who wanted to participate in the study completed the survey ( n = 167; response rate = 64.2%). We used a feedback survey to assess students' perceptions of the e-learning activity using four 5-point, Likert-type items. We also asked one open-ended question to solicit participants' feedback and suggestions for improving the e-learning activity. Descriptive statistics (frequency, mean [ SD ]) were used to summarize participants' perceptions and demographic characteristics. Content analysis was used to explore responses to the open-ended question. Results: Participants reported that the module increased their knowledge about health promotion, as well as their perceptions and confidence in working with older adults. Participants also found the method of instruction interactive and enjoyable. Conclusion: Our e-learning activity on health promotion was perceived by nursing students as helpful in sensitizing them to their role in promoting health among older adults. [ Journal of Gerontological Nursing, 50 (3), 19–24.]
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".