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Qualitative methods: the missing link in orthopaedic research.

Bone Joint Res

January 2025

The Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, London, United Kingdom.

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Importance: Mental health issues among young people are increasingly concerning. Conventional psychological interventions face challenges, including limited staffing, time commitment, and low completion rates.

Objective: To evaluate the effect of a low-intensity online intervention on young people in Hong Kong experiencing moderate or greater mental distress.

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Importance: There have been limited evaluations of the patients treated at academic and community hospitals. Understanding differences between academic and community hospitals has relevance for the design of clinical models of care, remuneration for clinical services, and health professional training programs.

Objective: To evaluate differences in complexity and clinical outcomes between patients admitted to general medical wards at academic and community hospitals.

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Purpose: This narrative review addresses post-2020, specific, complex challenges for use of and adherence to pre-exposure prophylaxis (PrEP) for HIV prevention among out-of-treatment people who use drugs (PWUD) at syringe services programs (SSPs).

Recent Findings: The COVID-19 pandemic and its associated changes to the provision of healthcare have significantly impacted HIV prevention, especially for PWUD. Through a synthesis of literature and clinical experience, we (1) characterize the operational changes imposed by the pandemic on SSPs that shaped the current HIV prevention landscape; (2) describe three levels of current challenges for PWUD, including consumer attitudes, non-medical and medical provider attitudes, and structural and scalability barriers; (3) characterize current models for PrEP in SSPs; and (4) offer practical recommendations for HIV prevention in harm reduction programs.

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Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging.

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