Co-design and evaluation of an audio podcast about sustainable development goals for undergraduate nursing and midwifery students
Bibliographic record
Abstract
Title: Co-design and evaluation of an audio podcast about sustainable development goals for undergraduate nursing and midwifery students Background The Sustainable Development Goals (SDGs) are global targets addressing poverty, inequality, and climate change. Despite their relevance to healthcare, nursing and midwifery students often have limited awareness of the SDGs. To help bridge this gap, a co-designed audio podcast was developed as an educational resource to strengthen students’ knowledge and highlight the goals’ professional relevance. Methods A prospective study was undertaken at Queen’s University Belfast with 566 first-year nursing and midwifery students. A 60-minute podcast, co-designed with students and stakeholders, was integrated into the university’s learning platform. Knowledge and attitudes toward the SDGs were assessed using pre- and post-test questionnaires. Six months later, 37 students participated in focus groups to reflect on their experiences. Quantitative data were analysed using paired t-tests and descriptive statistics, while qualitative data underwent thematic analysis. Results The podcast significantly improved students’ knowledge of the SDGs and their understanding of both professional and personal relevance. Post-test scores showed gains across all three domains: knowledge, professional relevance, and personal relevance. Students valued the podcast as an accessible and engaging learning tool, though some were unsure about replaying it. Focus groups highlighted three themes: ‘More than you know’ (expanded knowledge), ‘Nurse-Midwife Nudges’ (small behaviour changes), and ‘Fitting Format’ (preference for audio-based learning). Discussion Findings indicate that audio podcasts can be an effective and acceptable way to raise awareness of SDGs among nursing and midwifery students, positively shaping knowledge, attitudes, and behavioural intentions. The co-design approach proved valuable in tailoring the resource to learners’ needs and preferences.
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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 | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".