Evaluating the Appropriateness of Podcasts to Improve the Knowledge and Awareness of Selected Health Topics Among Undergraduate General Nursing Students: Protocol for an International Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Podcasts have proven to be a successful alternative source of educational material for students. Given the ability to listen to podcasts 24/7 and while on the go, this technology has the potential to provide informative and educational material to a large number of people at any given time. Podcasts are usually freely available on commonly used mobile devices, such as smartphones, laptops, and tablets. OBJECTIVE: This paper describes the impact of health-related podcasts as an intervention tool to support the knowledge and awareness of nursing students on a given topic. METHODS: Pre- and postpodcast questionnaires will gather data regarding the participants' knowledge and awareness of two topics-gestational diabetes and mental health. This intervention will be tested on general nursing undergraduate students. The total number of students (N=2395) from the participating universities are broken down as follows: (1) University College Cork (n=850) and the University of Galway (n=450) in Ireland, (2) Mzuzu University in Malawi (n=719), and (3) University of Fort Hare in South Africa (n=376). RESULTS: The study received ethical approval from the University College Cork Ethics Committee (2022-027A1). The approval obtained from University College Cork sufficed as ethics coverage for the University of Galway in Ireland. Ethics approval was also received from the Mzuzu University Research Ethics Committee (ID MZUNIREC/DOR/23/28) and the Inter-Faculty Research Ethics Committee of the University of Fort Hare (ID CIL002-21). Data collection is currently underway and will continue until the end of February 2024. The quantitative and qualitative data are expected to be analyzed in March 2024. CONCLUSIONS: Results from this study will allow for an investigation into the impact of podcasts in different settings: a high-income country (Ireland), an upper-middle-income country (South Africa), and a low-to-middle-income country (Malawi). The data gathered from this feasibility study will provide more clarity on the potential utility of podcasts as an intervention tool. We will gather data regarding listener demographics (eg, country of residence, age, gender, and year of study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50735.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".