The mental health of medical students is a crucial concern in contemporary medical education. This manuscript reports the availability and shape of psychological support for medical students in Poland in the backdrop of the COVID-19 pandemic and the Ukrainian crisis. A survey comprising 10 questions was distributed to 22 medical schools in Poland, with a 59% response rate. The findings reveal that psychological support programs are predominantly managed by internal university units. Funding sources vary, with some universities utilizing internal budgets and others leveraging European funds or external projects. The support modalities are primarily hybrid, catering to student preferences. Limitations exist in the number of consultations, although some universities provide extended support based on student needs. Assistance is offered in multiple languages, with additional programs like stress-reduction workshops and mindfulness training being available in several universities. A notable increase in demand for psychological support post-COVID-19 was reported, with some universities establishing their programs during or after the pandemic. The commentary highlights the rising need for mental health services among medical students and underscores the importance of flexible, inclusive, and well-funded support. The effective functioning of these programs aligns with the broader objective of fostering a resilient and emotionally balanced healthcare workforce.
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http://dx.doi.org/10.1186/s12909-024-06224-1 | DOI Listing |
The current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.
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Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field.
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