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- Towards a safer hospital environment: a multilevel analysis of safety climate and performancePublication . Ferreira, Juliana; Rodrigues, Matilde A.; Rodrigues, MatildeSafety climate is a crucial measure for organizations, predicting occupational accidents, monitoring safety performance, and identifying areas for improvement. However, its application in hospital settings remains limited. This study aimed to analyze safety climate levels and their relationship with other safety performance indicators in hospital settings using a multilevel perspective. The study was conducted in two hospital units located in Northern Portugal and involved 500 health professionals, including doctors, nurses, diagnosis and therapeutics technicians, operational assistants, and technical assistants. A questionnaire was applied to collect data of the variables under study. The questionnaire included a multilevel safety climate scale, which assesses the safety climate at the three different levels: management, team leader and coworkers. Additionally, it included scales to determine safety behaviours, motivation, time pressure and safety knowledge. Results revealed positive levels of safety climate across the three hierarchical subscales. Safety climate was positively associated with safety motivation, safety behaviors, and safety knowledge, and negatively associated with time pressure. Significant differences were observed across professional categories. Technical assistants were observed to reported lower levels for co-workers safety climate, safety knowledge, and safety participation, and higher time pressure. Additionally, work schedule was associated with safety climate perceptions and safety knowledge, with non-shift workers reporting higher safety climate at the management and team leader levels. This study enhances understanding of safety climate in hospital settings, highlighting the importance of addressing specific safety indicators, particularly among distinct professional groups.
- “Can a chatbot be used in the full-text screening in a systematic review?”Publication . Martins, André Miguel; Juan, Luis Félix Valero; Oliveira, Adriana; Martins, João Paulo; Santos, Marlene; Santos, Marlene; Oliveira Martins, João PauloLarge language model–based artificial intelligence tools are increasingly explored to support systematic reviews, yet evidence regarding their reliability in full-text screening remains limited. This study evaluated the performance of two versions of ChatGPT (4.0 and 5.0) compared with human reviewers during article selection for a systematic review on influenza vaccine effectiveness. A total of 170 full-text articles were independently assessed for eligibility using predefined inclusion and exclusion criteria. Human reviewers served as the gold standard. ChatGPT 4.0 and 5.0 were prompted using standardized instructions mirroring the review protocol. Agreement with human decisions was evaluated using accuracy, sensitivity, specificity, precision, F1-score, and Cohen’s κ. Intra-model reproducibility was assessed for ChatGPT 5.0. Results: ChatGPT 4.0 achieved an accuracy of 0.71 (95% CI: 0.64–0.78) and a Cohen’s κ of 0.43, indicating moderate agreement with human reviewers. ChatGPT 5.0 demonstrated improved performance, with accuracy increasing 0.06 to 0.77 (95% CI: 0.70–0.83), sensitivity of 0.87, specificity of 0.70, and κ of 0.55, corresponding to moderate-to-substantial agreement. Intra-model reproducibility for ChatGPT 5.0 showed 80% agreement (κ = 0.60), indicating partial but imperfect consistency. ChatGPT 5.0 outperformed ChatGPT 4.0 in full-text screening accuracy and reproducibility, approaching but not matching human performance. These findings support the use of current LLMs as decision- support tools rather than autonomous reviewers in systematic reviews. Transparent reporting of model versions, prompts, and input quality is essential to ensure credible AI-assisted evidence synthesis.
- Professionals’ perspectives on the role of advanced technologies in responsible gamblingPublication . Barroso, Beatriz C. R.; Cardoso, Leonor G.; Morgado, Pedro; Marques, António; Rocha, Carla; Griffiths, Mark D.; Queirós, Ricardo; Dores, Artemisa R.; Dores, ArtemisaProblematic gambling is associated with substantial individual and societal harms, which may be intensified by the accessibility, anonymity, and captivating design of online gambling products. In this domain, advanced technologies (e.g., artificial intelligence and data analytics) are increasingly recommended for prevention, early detection, and harm reduction. This study aimed to examine the current and potential uses of advanced technologies in gambling. Forty-five semi-structured interviews were conducted with social/legal science (n = 24) and technology (n = 21) experts. Data were analysed using thematic analysis, beginning with a deductive approach followed by an inductive analysis. A total of 182 codes were identified from social/legal science professionals, and 228 from technology experts. Four overarching themes were identified: (i) characterisation of contemporary gambling; (ii) applications and implications of advanced technologies; (iii) regulatory frameworks and challenges; and (iv) recommendations and solutions. Profit-driven design practices, heightened vulnerabilities in online gambling environments, regulatory gaps, and concerns regarding algorithmic opacity and data use were highlighted. Advanced technologies were viewed as having a dual role, with potential both to exacerbate gambling-related harm and to enable earlier identification of risky behaviours and targeted interventions. Findings indicate that responsible gambling cannot rely solely on technological solutions (e.g., behavioural monitoring and limit-setting) and should be instead integrated with community-level strategies, public education, and robust regulatory oversight. Leveraging technological innovation alongside user protection is essential, requiring interdisciplinary collaboration, a public health-oriented approach, and transparent, evidence-based and ethically governed frameworks. Implications, limitations, and future directions are discussed.
- Towards a multidimensional Model of Neurocognitive Disorders (MOND Model): Integrating evidence from a critical review into a model for future researchPublication . Pinto, Joana O.; Peixoto, Bruno; Dores, Artemisa R.; Barbosa, Fernando; Dores, ArtemisaThe main purpose of this work is to critically review the literature on neurocognitive disorders (ND) diagnosis. A critical review was conducted in PubMed, Scopus, and EBSCO. Systematic reviews and meta-analyses focusing on ND diagnosis were included. The selected studies were critically analyzed and conceptually integrated to identify relevant dimensions for the diagnosis of ND. The review included 88 studies. Most studies focused on Alzheimer’s disease and mild cognitive impairment. The literature remained predominantly centred on isolated diagnostic domains, and important limitations were consistently identified, including methodological heterogeneity, lack of standardized thresholds, and reduced clinical applicability. Based on the identified conceptual and methodological limitations, a Multidimensional Model of Neurocognitive Disorders (MOND model) for ND diagnosis was proposed. The MOND model was developed as a multidimensional, multilevel, transdiagnostic model integrating biological, neurocognitive, neuropsychiatric, motor, functional, frailty, reserve-related, and socio-environmental dimensions. The model may contribute to research, symptom classification, severity characterization, prognosis, and personalized intervention planning across different ND trajectories. Future studies using the MOND model should focus on refining algorithms to estimate the risk of ND.