JOURNAL ARTICLE
Exploring students' experiences of a blended learning pre-registration midwifery education programme.
Published In: British Journal of Midwifery, 2025, v. 33, n. 10. P. 550 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Nash, Helena; Hayward, Melanie 3 of 3
Abstract
Background/Aims: Blended learning is increasingly adopted in pre-registration midwifery education because of its pedagogical benefits, yet little research explores student midwives' lived experiences of such programmes. This study examined the experiences of final-year student midwives undertaking a 3-year blended learning programme, to enhance educator understanding. Methods: This qualitative interpretative phenomenological study involved four purposively selected students from a university in southeast England. Semi-structured interviews were conducted, transcribed verbatim and analysed using interpretative phenomenological analysis. Results: Four experiential themes were found: the safety and welfare of learning, ownership and autonomy, the gravitational pull of friendships, and the nurturing aspect of peer relationships. Students reported increased self-efficacy, academic and professional growth, and the significance of connection and belonging. Conclusions: Conclusions Blended learning supports recruitment, retention and student wellbeing, encouraging a compassionate learning environment that enhances preparedness for midwifery practice. Implications for practice: It is suggested that blended learning supports the development of compassionate and more autonomous individuals who are equipped to work as professional midwives in a digitally evolving clinical environment. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:British Journal of Midwifery. 2025/10, Vol. 33, Issue 10, p550
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:0969-4900
- DOI:10.12968/bjom.2025.0019
- Accession Number:188450157
- Copyright Statement:Copyright of British Journal of Midwifery is the property of Mark Allen Holdings Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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