Nursing Students’ Views on an E-Learning Activity on Clinical Leadership and Ageism: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Students require knowledge and skills in clinical leadership in order to address the issue of ageism. An e-learning module was developed that used ageism as an exemplar to practise using the skills and knowledge needed for clinical leadership. Ageism was chosen because it is prevalent in nursing culture and nursing education and influences nursing practice with older people. Purpose: The aim of this study was to understand nursing students’ perspectives about how a clinical leadership and ageism e-learning module enhanced their knowledge, confidence, and ability to use clinical leadership to address ageism, and whether the module was an enjoyable method of learning the material. Methods: A cross-sectional study was used to understand nursing students’ perceptions of the e-learning module. A Likert-style post-learning survey was completed by 67 students. Results: Students were highly satisfied with the e-learning module’s ability to enhance their knowledge, perception of working with older people, and sense of confidence in using the strategies in their own practice. They found the learning activity enjoyable and commented on their appreciation of the self-paced learning activity, applied scenarios, and interactive elements. Conclusion: Delivery of gerontological concepts in nursing programs is essential to prepare students to work with the population most likely to be encountered in most practice settings—older people. Clinical leadership concepts provide a meaningful and practical method to address ageism in practice, which is confirmed through students’ increased self-ratings of both knowledge and confidence after completing the learning activity. This e-learning module provides an unbiased, evidence-based, learner-paced activity that may be used in a variety of settings and demonstrates high acceptability for nursing students. Implications: Nursing students must be aware of the prevalence and negative impact of ageism so that they can take leadership in challenging practices and messages that devalue older people. To prepare nurses for the realities of the workforce and an ageing population, nursing education must employ strategies for direct and applied approach of practice standards, such as the Entry-to-Practice Gerontological Care Competencies for Baccalaureate Programs in Nursing (Canadian Association of Schools of Nursing, 2017).
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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 | 0.005 |
| 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".