Unlabelled: Background As efforts continue to increase rates of HIV testing and condom use among at-risk communities in England, organisations have sought use social media for health promotion interventions. As part of a wider evaluation of It Starts With Me (ISWM), a sexual health promotion intervention in England targeting gay and bisexual men and African people through Facebook, this study sought to explore how the online environment shapes end user engagement with sexual health interventions. A primary objective was to explore how privacy concerns can act as a barrier to engagement for the audience of ISWM.
Methods: A purposive sample of 40 individuals were recruited, who were targeted by the intervention for in-depth interviews. Data collection was in two phases. In the first phase, individuals were sampled based on engagement with online health interventions in general, while in the second phase, all individuals were sampled on the basis of engagement with the intervention.
Results: Privacy concerns related to the ecology of social networking sites, issues with implied disclosure and discrimination, as well as uncertainty over control of data. These concerns limited the organic reach of the intervention by confining the intervention to those who already held the norms diffused through it, and by discouraging participants from sharing and commenting on content.
Conclusions: Care should be taken to address concerns when designing interventions delivered through social media. Gated interventions may be more beneficial for marginalised communities, while large-scale interventions such as ISWM may provide a useful backdrop for face-to-face interventions.
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http://dx.doi.org/10.1071/SH15231 | DOI Listing |
BMC Nurs
January 2025
Nursing Department, Hamad Medical Corporation, Doha, P.O. Box 3050, Qatar.
Background: Artificial Intelligence (AI) is increasingly applied in healthcare to boost productivity, reduce administrative workloads, and improve patient outcomes. In nursing, AI offers both opportunities and challenges. This study explores nurses' perspectives on implementing AI in nursing practice within the context of Jordan, focusing on the perceived benefits and concerns related to its integration.
View Article and Find Full Text PDFBMC Urol
January 2025
Department of Urology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Nan Li Shi Lu Street No.56, Beijing, 100045, China.
Background: To analyze the clinical characteristics, complications and patients satisfaction of MIP hypospadias variant.
Methods: A retrospective analysis was performed for 31 patients with MIP admitted to our hospital from January 2008 to February 2023. All enrolled patients underwent telephone follow-up and a survey was conducted on the satisfaction of patients and their families.
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
View Article and Find Full Text PDFNeural Netw
January 2025
Luca Healthcare R&D, Shanghai, 200000, China. Electronic address:
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to train target models by mitigating biases induced by noisy pseudo labels, they often lack theoretical guarantees for fully reducing biases and have predominantly addressed classification tasks rather than regression ones. To address these gaps, our analysis delves into the generalisation error bound of the target model, aiming to understand the intrinsic limitations of pseudo-label-based SFUDA methods.
View Article and Find Full Text PDFJ Gambl Stud
January 2025
Flinders Health and Medical Research Institute, Rural and Remote Health, Flinders University, Charles Darwin University, PO Box U362 PO Box 42500, Casuarina, NT, 0815, Australia.
This study provides an in-depth qualitative exploration of Aboriginal peoples' experiences with seeking help for gambling-related issues in the Northern Territory (NT), Australia. Through semi-structured interviews with 29 participants, including regular and occasional gamblers as well as those affected by others' gambling, the research highlights key barriers to seeking formal help. These barriers included the normalisation of gambling within Aboriginal communities, denial of gambling problems, feelings of shame, privacy concerns, and a lack of trust in mainstream services.
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