https://www.um.edu.mt/library/oar/handle/123456789/318 2026-10-08T23:50:43Z https://www.um.edu.mt/library/oar/handle/123456789/149728 Title: Explainable machine learning predictive models for surgical site infections : scoping review Authors: Sun, Rui; Liu, Yao; Lu, Shuya; Tartari, Ermira; Birgand, Gabriel; Yang, Lin; Zhou, Lei Abstract: Background: Surgical site infections (SSIs) remain a major cause of health care–associated infections, and early prediction is essential for improving patient outcomes. Machine learning (ML) has shown potential for SSI prediction; however, clinical implementation requires models that are both accurate and explainable. Despite recent progress in explainable ML, its clinical application to SSI prediction remains limited.; Objective: This study aimed to map explainable ML models for SSI prediction from a clinical perspective and examine their use of structured and unstructured data across the dimensions of data, methodology, and explanation output.; Methods: We conducted a scoping review following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Joanna Briggs Institute (JBI) guidance, and registered the protocol in PROSPERO. Six databases were searched for eligible studies published from January 2010 onward, without language restrictions. The search was conducted on August 9, 2025, and updated on July 14, 2026. We included studies that developed or validated an explainable ML model for predicting SSI in adults. Two reviewers (RS and YL) independently screened studies and extracted data. Findings were narratively synthesized and presented in evidence maps. Methodological quality was assessed using the PROBAST+AI (Prediction model Risk Of Bias Assessment Tool for prediction models using regression or artificial intelligence) methods.; Results: Overall, 77 studies reporting 98 ML models were included. Most models were prognostic (72/98, 73.5%), whereas 26 focused on postoperative SSI diagnosis. A total of 81.8% (63/77) of studies addressed a single surgical specialty, most commonly gastrointestinal surgery (27/63, 42.9%). Overall, 51.9% (40/77) of studies addressed composite SSI predictions. Among all models, % (40/98) were black-box models explained by post hoc methods; SHAP combined with ensemble learning was the leading approach (18/40, 45%). Regression models accounted for half of the inherently interpretable models, interpreted using coefficients. Prognostic models commonly included health and lifestyle (60/72, 83.3%), individual characteristics, and surgical process details (both 57/72, 79.2%); health and lifestyle factors were most frequently important across SSI types.Diagnostic models commonly included surgical process details (13/26, 50%), administrative codes, and individual characteristics (both 10/26, 38.5%). Key diagnostic predictors varied by SSI types: postoperative clinical interventions predominated for composite SSI; vital signs, postoperative interventions, and administrative codes for superficial SSI; postoperative recovery status for deep SSI; and vital signs for organ-space SSI.; Conclusions: Extending previous reviews focusing on model performance, this review mapped explainability methods and important features in SSI prediction, identifying recurring predictor patterns and substantial methodological heterogeneity across prognostic and diagnostic settings. Incomplete reporting of feature definitions and explanatory rationale, together with limited clinical relevance, constrained clinical interpretation and actionability. Clinician-informed reporting frameworks and validation of explanation fidelity and clinical relevance are needed to improve the trustworthiness and utility of SSI prediction models. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/149530 Title: Public awareness and knowledge of antibiotic use and antimicrobial resistance in Malta Authors: Schranz, Gavin; Camilleri, Vanessa; Sciortino, Monique; Borg, Michael Angelo; Tartari, Ermira Abstract: Introduction Antimicrobial resistance (AMR) is a major global threat, driven partly by inappropriate antibiotic use. Understanding public knowledge, awareness, and behaviours is critical for designing effective interventions. This study assessed these dimensions within the Maltese population to inform national AMR awareness and stewardship strategies.; Method A cross-sectional online survey was conducted between 9 and 29 April 2024 among Maltese residents using a questionnaire adapted from the World Health Organization (WHO) Antibiotic Resistance Multi-Coun try Public Awareness Survey. A convenience sampling approach with active demographic monitoring was used across gender, age, and education groups, using census-derived targets as benchmarks. The survey, available in English and Maltese, assessed knowledge of appropriate antibiotic use, awareness of AMR-related terminology, and attitudes towards antibiotic use and resistance. Data were analysed using descriptive statistics and non-para metric tests (Mann-Whitney U, Kruskal-Wallis H, Chi-Square, Likelihood Ratio) to explore associations between demographic factors and knowledge or behavioural outcomes.; Results A total of 476 eligible respondents completed the survey and were included in the analysis. Among respondents, 59.9% were female and the largest age group was 25–34 years (31.9%). Awareness of “antibiotic resistance” was high (88%) yet understanding of “antimicrobial resistance” (48%) and its abbreviation “AMR” (21%) was lower. Most participants (97%) agreed that antibiotics should only be used when prescribed, but mis conceptions persisted: 16.6% believed antibiotics treat colds and flu, 33.6% for fever, and 39.9% for sore throats. Three-quarters (75%) incorrectly attributed resistance to the body becoming resistant rather than bacteria. Older and more educated respondents showed significantly better knowledge and more appropriate attitudes (p < 0.05), whereas younger adults and males were more likely to hold misconceptions. While nearly all respondents (96.7%) agreed that individuals should use antibiotics responsibly, fewer (53.4%) viewed AMR as a major global chal lenge or expressed personal concern (67.2%) about its impact, and one-third believed they were not at risk if they used antibiotics correctly. Findings indicate general awareness but persistent conceptual and demographic gaps.; Conclusion Despite high awareness of appropriate antibiotic use, misconceptions about treatment indications and the mechanisms of AMR remain, particularly among younger adults and those with lower education. Improving public understanding of when antibiotics are needed and how resistance develops will be critical to promoting responsible use. These findings provide locally generated evidence to inform targeted public health communication and support AMR prevention efforts in Malta. 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/148924 Title: Identifying the competencies for developing a nursing competence tool through a Delphi study approach Authors: Axiak, Geoffrey; Mamo, Julian; Scicluna Ward, Corinne Abstract: Competencies form the basis of up-to-date practice. In today’s world it is expected that anyone practicing any profession will be proficient and professional. That is why assessing the competency of professionals, including nurses, is critical to maintaining a safe and professional service. This study aims to identify nursing competencies that are relevant to all areas of nursing. This article will outline the literature search undertaken plus the other processes that the authors undertook to identify the competencies relevant to nursing in Malta. 2026-03-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/148923 Title: Digital competency nursing self-assessment tool (DiCoNSaT) : development and validation of a nursing digital competency assessment instrument : a conference report Abstract: The presentation introduced the Digital Competency Nursing Self-Assessment Tool (DiCoNSaT), a doctoral research project undertaken at the University of Malta aimed at developing and validating a comprehensive instrument for assessing nursing competence within digitally enabled healthcare environments. The project responds to the growing need for standardized methods of evaluating nursing competencies amidst increasing technological integration in healthcare systems. 2026-01-01T00:00:00Z