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Residents' perceptions of impaired wellness in China:Accepting the inevitable, questioning the preventable
No full textIntroduction Impaired wellness among residents has become a global concern, with burnout, stress and fatigue linked to negative outcomes for both residents and patients. To date, most of the existing research has come from Western contexts, where cultural norms and training structures may significantly differ from those in other regions. However, there remains limited understanding of how residents in non-Western settings experience and interpret impaired wellness. This study aims to explore the perceptions and experiences of residents' impaired wellness within the context of residency training in China.Methods We conducted a constructivist qualitative study. Participants were recruited through purposive and snowball sampling from a teaching hospital in Shanghai. Semi-structured interviews were conducted in Chinese between March 2024 and February 2025, guided by a six-dimensional wellness framework developed from existing literature. We used reflexive thematic analysis to analyse the data both deductively and inductively.Results Chinese residents perceived some degree of wellness impairment across physical, psychological and social dimension as acceptable, often framing such impairments as contributing to professional and personal growth, reflecting cultural values emphasising acceptance of and growth through hardship. In contrast, impairments in the intellectual and financial dimensions, exacerbated by unfair compensation, limited supervision and research pressure, were seen as unreasonable yet preventable. Residents' recognition of these challenges as rooted in systemic and structural conditions of residency training, largely beyond their control, often led to resignation, passive endurance, and in some cases, consideration of leaving the profession. Harmonious work relationships were described as central to navigating impaired wellness, serving as vital buffers when present but vulnerabilities when absent, largely echoing cultural ideals of harmony.Discussion This study sheds light on how the inherently demanding nature of clinical practice, cultural values and local systemic and structural conditions of residency training intersect to shape residents' perceptions and experiences of impaired wellness
Artificial intelligence characters are dangerous without legal guardrails
No full textOnline interactions with artificial intelligence (AI) characters pose serious risks to humans who begin to trust them. Currently, there is a low barrier to access AI characters, and regulations fail to adequately protect users online. We discuss the specific risks of AI characters, the regulatory framework and potential avenues for mitigating harm.</p
Pilot ‘Positief Gezond Werken in het MBO’:Begeleidend Onderzoek
Get PDFMBO-scholen hebben ervaring met Positieve Gezondheid (PG) bij studenten en willen de toepassing voor medewerkers verder ontwikkelen
From truth to trickery: A scoping review of (deceptively) misrepresented evidence in suspect interviews
No full textITEM Reflection: Elections of the House of Representatives of the Netherlands on 29 October 2025 from a Cross-Border Perspective
Get PDFRainbow Europe or Rainbow Washing?:A Comment on the EU’s updated LGBTIQ+ Equality Strategy
No full textA growing number of EU Member States have proposed or introduced legislation that directly targets LGBTIQA+ individuals. What a decade ago seemed to be Hungary’s isolated case is today a growing trend across the Union. Against this backdrop, the European Commission just published its new LGBTIQ+ Equality Strategy (2026-2030). In my view, however, this initiative represents a downgraded commitment of the European Commission towards the protection of Queer individuals
Healthcare professionals' perspectives on artificial intelligence in patient care:a systematic review of hindering and facilitating factors on different levels
No full textBackgroundArtificial intelligence (AI) applications present opportunities to enhance the diagnosis, prognosis, and treatment of various diseases. To successfully integrate and utilize AI in healthcare, it is crucial to understand the perspectives of healthcare professionals and to address challenges they associate with AI adoption at an early stage. Therefore, the aim of this review is to provide a comprehensive overview of empirical studies that explore healthcare professionals' perspectives on AI in healthcare.MethodsThe review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. The databases MEDLINE, PsycINFO, and Web of Science were searched in the timeline of 2017 to 2024 using terms related to 'healthcare professionals', 'artificial intelligence', and 'perspectives'. Eligible were peer-reviewed articles that employed quantitative, qualitative, or mixed-methods approaches. Extracted facilitating and hindering factors were analysed according to the dimensions of the socio-ecological model.ResultsOur search yielded 4,499 articles published up to February 2024. After title abstract screening, 150 full-texts were assessed for eligibility, and 72 studies were ultimately included in our synthesis. The extracted perspectives on AI were thematically analyzed using the socioecological model in order to identify various levels of influence and to categorize them into facilitating and hindering factors. In total, we identified 49 facilitating and 43 hindering factors across all levels of the socioecological model. ConclusionsThe findings from this review can serve as a foundation for developing guidelines for AI implementation adressing various stakeholders, from healthcare professionals to policymakers. Future research should focus on the empirical adoption of AI applications and, if possible, further examine the hindering factors associated with different types of AI
A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data:Model Development and Validation
No full textBACKGROUND: The COVID-19 pandemic has highlighted the need for robust and adaptable diagnostic tools capable of detecting the disease from diverse and continuously evolving data sources. Machine learning models, particularly convolutional neural networks, are promising in this regard. However, the dynamic nature of real-world data can lead to model drift, where the model's performance degrades over time, as the underlying data distribution changes due to evolving disease characteristics, demographic shifts, and variations in recording conditions. Addressing this challenge is crucial to maintaining the accuracy and reliability of these models in ongoing diagnostic applications. OBJECTIVE: This study aims to develop a comprehensive framework that not only monitors model drift over time but also uses adaptation mechanisms to mitigate performance fluctuations in COVID-19 detection models trained on dynamic cough audio data. METHODS: Two crowdsourced COVID-19 audio datasets, namely COVID-19 Sounds and Coswara, were used for development and evaluation purposes. Each dataset was divided into 2 distinct periods, namely the development period and postdevelopment period. A baseline convolutional neural network model was initially trained and evaluated using data (ie, coughs from COVID-19 Sounds and shallow coughs from Coswara dataset) from the development period. To detect changes in data distributions and the model's performance between these periods, the maximum mean discrepancy distance was used. Upon detecting significant drift, a retraining procedure was triggered to update the baseline model. The study explored 2 model adaptation approaches, unsupervised domain adaptation and active learning, both of which were comparatively assessed. RESULTS: The baseline model achieved an area under the receiver operating characteristic curve of 69.13% and a balanced accuracy of 63.38% on the development test set of the COVID-19 Sounds dataset, while for the Coswara dataset, the corresponding values were 66.8% and 61.64%. A decline in performance was observed when the model was evaluated on data from the postdevelopment period, indicating the presence of model drift. The application of the unsupervised domain adaptation approach led to performance improvement in terms of balanced accuracy by up to 22% and 24% for the COVID-19 Sounds and Coswara datasets, respectively. The active learning approach yielded even greater improvement, corresponding to a balanced accuracy increase of up to 30% and 60% for the 2 datasets, respectively. CONCLUSIONS: The proposed framework successfully addresses the challenge of model drift in COVID-19 detection by enabling continuous adaptation to evolving data distributions. This approach ensures sustained model performance over time, contributing to the development of robust and adaptable diagnostic tools for COVID-19 and potentially other infectious diseases