Evaluation of a delirium awareness podcast for undergraduate nursing students in Northern Ireland: a pre−/post-test study
Bibliographic record
Abstract
BACKGROUND: Delirium is a common disorder affecting several people in primary, secondary, and tertiary settings. The condition is frequently under-diagnosed leading to long-lasting physical and cognitive impairment or premature death. Despite this, there has been limited research on the impact of innovative approaches to delirium education amongst undergraduate nursing students. The aim of this study was to evaluate the effect of a delirium awareness podcast on undergraduate nursing student knowledge and confidence related to the condition in Northern Ireland. METHODS: The intervention was a 60-min delirium awareness podcast, available throughout May 2020, to a convenience sample of year one undergraduate nursing students (n = 320) completing a BSc Honours Nursing degree programme in a Northern Ireland University. The podcast focused on how nursing students could effectively recognise, manage, and prevent delirium. Participants had a period of 4 weeks to listen to the podcast and complete the pre and post questionnaires. The questionnaires were comprised of a 35-item true-false Delirium Knowledge Questionnaire (DKQ), a 3-item questionnaire about professional confidence and a 7-item questionnaire evaluating the use of podcasting as an approach to promote knowledge and confidence about delirium. Data were analysed using paired t-tests and descriptive statistics. RESULTS: Students improved across all three core areas in the post-test questionnaire, demonstrating improvements in knowledge about symptoms of delirium (7.78% increase), causes and risk factors of delirium (13.34% increase) and management of delirium (12.81% increase). In relation to perceived confidence, students reported a 46.50% increase in confidence related to recognition of delirium, a 48.32% increase in relation to delirium management and a 50.71% increase their ability to communicate about delirium. Both questionnaires were statistically significant (P < 0.001). The final questionnaire illustrated that nursing students positively evaluated the use of podcast for promoting their knowledge and confidence about delirium and 96.32% of nursing students believed that the podcast met their learning needs about delirium. CONCLUSIONS: A 60-min podcast on delirium improved first year student nurse knowledge about delirium. Nursing students also expressed that this approach to delirium education was effective in their learning about the condition.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".