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To justify the use of focus group interviews with patients and healthcare professionals within a phenomenological-hermeneutic approach inspired by the theory of the French philosopher Paul Gustave Ricoeur.
Methodological guidance and discussion grounded in Ricoeur's theory on narrative, dialogue, threefold mimesis and interpretation.
This phenomenological-hermeneutical approach to focus group interviews yields significant, in-depth understandings of lived experiences from both patients and healthcare professionals.
Ricoeur-inspired phenomenological-hermeneutical focus group interviews with patients and healthcare professionals offer a promising approach for exploring and generating new, valuable insights into the complexities of clinical nursing practice. Thus, this paper argues for an integration of focus group interviews and a phenomenological-hermeneutical approach within nursing science.
The approach has significant implications for nursing practice. By incorporating Ricoeur-inspired dialogue-based collective voices of patients and healthcare professionals in focus group interviews, nursing practices can be refined, leading to improved patient care and more effective clinical interventions. Thus, this approach advocates for a broader adoption of Ricoeur-inspired focus group interviews in nursing research and in health research in general to enhance the understanding and development of clinical models.
No available EQUATOR guidelines were applicable to this methodological paper, as no new data were created or analysed.
As this is a methodological paper, no new patient or public contributions are included.
by Ibrahim Naif Alenezi, Fathia Ahmed Mersal, Amal Ahmed Elbilgahy
BackgroundGenerative artificial intelligence (AI) tools such as ChatGPT are increasingly used in academic research, yet evidence on postgraduate students’ perceptions remains limited in non-Western and health-professional contexts. Understanding how students perceive AI’s benefits, risks, and ethical implications is essential for informing institutional research policies.
MethodsThis cross-sectional case study surveyed 267 master’s students enrolled in nursing and health profession programs at Northern Border University in Arar, Saudi Arabia. Data were collected between October 1 and November 15, 2025, using a validated 54-item questionnaire that assessed perceived benefits, perceived risks, privacy concerns, mistrust in AI, performance anxiety, social bias, regulatory matters, liability issues, and intention to adopt AI tools. Multiple linear regression with heteroscedasticity-robust (HC3) standard errors was used to identify predictors of AI adoption intention.
ResultsMost participants (85.0%) reported prior use of AI tools, predominantly ChatGPT. Perceived benefits were the strongest predictor of intention to adopt AI for research purposes (β = 0.588, p 2 = 0.560).
ConclusionsAmong nursing and health profession master’s students at a regional Saudi university, findings indicate pragmatic optimism toward AI integration in academic research, driven primarily by perceived benefits alongside heightened ethical and privacy awareness. Privacy concerns appear to reflect critical literacy rather than barriers to adoption.
To assess the relationship between paradoxical leadership and nurses' positive attitudes towards artificial intelligence in hospital settings through a strengths mindset as a mediator.
A cross-sectional survey conducted from January to March 2024.
The study included 239 nurses from four hospitals in Port Said, Egypt. To measure the study constructs, three well-established scales were utilised: the Paradoxical Leadership Scale, the Strengths Mindset Scale and the Positive Attitudes Towards Artificial Intelligence Scale. Structural equation modelling was applied for data analysis.
The analysis revealed a significant positive relationship between nurse managers' paradoxical leadership and nurses' positive attitudes towards artificial intelligence. Additionally, a strengths mindset partially mediated the relationship between paradoxical leadership and nurses' positive attitudes towards artificial intelligence.
The study findings suggest that developing paradoxical leadership behaviours—such as managing current work processes while simultaneously driving the exploration of new initiatives—among nurse managers can foster a strengths mindset in nurses, which in turn promotes a more positive attitude towards the integration of artificial intelligence in healthcare.
This study enhances the understanding of how paradoxical leadership influences nurses' acceptance of artificial intelligence, underscoring the pivotal role of a strengths mindset in this process.
This study suggests that healthcare policymakers seeking smoother integration of artificial intelligence technologies among nurses should prioritise leadership development programmes that equip nurse managers with paradoxical leadership skills and implement training initiatives to strengthen nurses' mindsets.
The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology checklist.
No patient or public contribution.
Deep vein thrombosis (DVT) in critically ill patients is often undetected. However, it is unclear whether ultrasound surveillance for early detection of DVT in high-risk medical-surgical intensive care unit (ICU) patients improves patients’ outcomes. The DETECT trial (Diagnosing deep-vein thrombosis early in critically ill patients) evaluates the effect of twice-weekly bilateral lower limb ultrasound compared to usual care on 90-day mortality of critically ill adult patients admitted to medical, surgical and trauma ICUs.
The DETECT trial is an international, parallel-group, open-label, randomised trial, which will recruit 1800 critically ill adults from over 14 hospitals in Saudi Arabia and Kuwait. Eligible patients will be allocated to twice-weekly bilateral lower limb ultrasound or usual care. The primary outcome is 90-day mortality. Secondary outcomes include lower limb proximal DVT, pulmonary embolism and clinically important bleeding. The first patient was enrolled on 21 March 2023. As of 8 April 2025, 711 patients have been enrolled from 14 centres in Saudi Arabia and Kuwait. The first interim analysis was conducted on 14 May 2025. We expect to complete recruitment by December 2026.
Institutional review boards (IRBs) of each participating institution approved the study. We plan to publish the results in peer-reviewed journals and present the findings at international critical care conferences.
Clinicaltrials.gov: NCT05112705, registered on 9-11-2021.
Diabetes mellitus (DM) and depression commonly coexist. Each condition increases the risk of developing the other and adversely affects treatment outcomes. Such complex interactions of diseases, referred to as syndemics, have not been well studied. This study aims to assess the syndemics of depression, sick role and activation status among newly diagnosed adults living with DM.
A prospective 6-month follow-up study will be conducted with 485 participants. Depression will be assessed with the 9-item Patient Health Questionnaire, applying a cut-off score of 10. The primary outcome will be glycaemic control, and the secondary outcomes will be health-related quality of life (HRQoL) and functional disability status. Depression, the primary outcome and the secondary outcomes, will be measured at baseline, 3 months and 6 months. The sick role, activation status and health system perspectives will be explored using qualitative methods following the second measurement. Data will be collected from adults living with DM, healthcare providers and healthcare managers. Qualitative sampling will continue until data saturation is reached.
Quantitative analysis will be done using STATA V.17. The prevalence of depression will be determined at baseline. Associated factors will be analysed using Poisson regression with a robust variance estimator. Incidence rate of depression, glycaemic control, HRQoL and disability status will be measured at 3 and 6 months. A multilevel mixed-effects generalised linear model will be fitted, with the three measurement time points nested within individuals, and individuals nested within health institutions. Qualitative data will be analysed thematically using NVivo V.12 software.
Ethics approval has been granted by the institutional review board of Bahir Dar University (protocol number 3098/25). Findings will be disseminated through peer-reviewed publications, conference presentations and local channels for community audiences.
Protocol number 3098/25.
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