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To explore nursing students' experience of how the COVID-19 pandemic influenced the learning environment in clinical setting and how they responded to the change.
Qualitative descriptive interview study.
Eleven third-year baccalaureate nursing students from a University College in Northern Denmark participated in individual semi-structured interviews conducted in the spring 2021. Data analysis was guided by Braun and Clarke's thematic analysis.
Two themes were generated: ‘A compromised learning environment’ and ‘Adjusting to circumstances and making things work’.
Students perceived that their learning became secondary and was influenced negatively. Some students' focus shifted from an attention to learning opportunities to managing daily patient care and became hesitant to pose questions that were relevant to their learning. Students faced a dilemma between helping with daily tasks and prioritising their learning needs. Some students adjusted to the circumstances by taking the lead. Others reacted more passively and prioritised helping nurses in managing the daily workload.
The study highlights that changes in the work environment impact students. Institutions must ensure that students feel a sense of belonging and prioritise time with clinical supervisors for questions and reflection, avoiding situations where practical tasks take priority over learning. Since students manage changes differently, they require tailored support.
The study addressed changes in the clinical learning environment caused by the COVID-19 pandemic. The changes within the environment influenced the students learning negatively. The findings are of relevance to lecturers, supervisors, and academic decision-makers within nursing education and may guide the planning of clinical placements to better accommodate individual learning needs.
The study adheres to the COREQ guidelines.
This study did not include patient or public involvement in its design, conduct, or reporting.
To examine the feasibility of using a large language model (LLM) as a screening tool during structured literature reviews to facilitate evidence-based practice.
A proof-of-concept study.
This paper outlines an innovative method of abstract screening using ChatGPT and computer coding for large scale, effective and efficient abstract screening. The authors, new to ChatGPT and computer coding, used online education and ChatGPT to upskill. The method was empirically tested using 400 abstracts relating to public involvement in nursing education from four different databases (CINAHL, Scopus, ERIC and MEDLINE), using four versions of ChatGPT. Results were compared with a human nursing researcher and reported using the CONSORT 2010 extension for pilot and feasibility trials checklist.
ChatGPT-3.5 Turbo was most effective for rapid screening and had a broad inclusionary approach with a false-negative rate lower than the human researcher. More recent versions of ChatGPT-4, 4 Turbo, and 4 omni were less effective and had a higher number of false negatives compared to ChatGPT-3.5 Turbo and the human researcher. These more recent versions of ChatGPT did not appear to appreciate the nuance and complexities of concepts that underpin nursing practice.
LLMs can be useful in reducing the time nurses spend screening research abstracts without compromising on literature review quality, indicating the potential for expedited synthesis of research evidence to bridge the research–practice gap. However, the benefits of using LLMs can only be realised if nurses actively engage with LLMs, explore LLMs' capabilities to address complex nursing issues, and report on their findings.
Nurses need to engage with LLMs to explore their capabilities and suitability for nursing purposes.
No patient or public contribution.
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