Improving student nurses’ perspectives towards older people with an e‐learning activity: A quasi‐experimental pre‐post design
Bibliographic record
Abstract
BACKGROUND: Despite older people being the largest demographic accessing health care, nurses often lack knowledge about how to work with them and may hold ageist perceptions towards them. Previous research has identified the gaps in their education program and offered suggestions on what and how to fill those gaps in education related to older people. E-learning activities to fill these gaps were developed. OBJECTIVE: The aim of this study was to determine if nursing students' perceptions about older people could be improved through an e-learning activity focused on communication and understanding older people. METHODS: A quasi-experimental pre-post design was used to test whether the understanding and communication with older people e-learning activity improved student nurses' perceptions about older people. A feedback survey was also analyzed using descriptive statistics to understand students' perceptions of the learning activity. RESULTS: There was a statistically significant decrease in participant's negative perceptions towards older people after completing the e-learning activity. Participants enjoyed the activity and believed that it improved their knowledge of older people, their confidence in working with older people, and their perceptions about older people. CONCLUSIONS: The strength of the e-learning activity in this study is that the educator need not be an expert in order to use the activity in their course. In this way, knowledge about older people is facilitated despite the dearth of nurse educators with gerontological expertise. More research to test this activity in other universities is needed. IMPLICATIONS FOR PRACTICE: Improved understanding and communicating with older people could improve person-centered-care. The flexible delivery of this learning activity could facilitate practicing nurses understanding and communication strategies if offered to them.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".