Browsing by Author "Castelo-Branco, Miguel"
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- Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysisPublication . Rocha, Ana; Costeira, Cristina; Barbosa, Raul; Gonçalves, Florbela; Castelo-Branco, Miguel; Viana, Joaquim; Gaudêncio, Margarida; Ventura, FilipaBackground Oncology nurses face unique and intense demands due to the nature of their work, caring for patients with life-threatening illnesses. The emergence of professional burnout among these nurses is influenced by several factors, highlighting the importance of identifying protective and risk factors to mitigate its impact. This study aims to identify burnout profiles and protective socio-demographic and work-related patterns associated with reduced burnout among oncology nurses. Methods A cross-sectional study was conducted with 150 oncology nurses at a specialized hospital exclusively dedicated to adult oncology treatment in Portugal. Data collection included a self-administered questionnaire incorporating the validated Portuguese version of Maslach Burnout Inventory (MBI). Statistical analyses were performed using SPSS and machine learning tools, specifically KMeans clustering and Random Forest algorithms. Results Six protective patterns against burnout were identified, characterized by conditions of permanent contracts, work-life balance, and supportive work environments. Moreover, factors such as holding management roles and being a parent of two or more children might even be protective in some circumstances, suggesting a nuanced relation between personal and professional factors. Machine learning analyses made apparent the unpredictability of burnout and highlighted the critical role of protective factors in mitigating its impact. Conclusions This study underscores the importance of resilience-building strategies and promoting protective factors, such as job stability, learned experience, and adequate rest, to reduce burnout risk among oncology nurses. Future research should validate these findings through hypothesis-driven analyses to inform targeted and context-specific burnout prevention programs.
- Quantification and Modulation of Tremor in Rapid Upper Limb MovementsPublication . Faria, Paula; Leal, Adriana; Freire, António; Januário, Cristina; Patrício, Miguel; Castelo-Branco, MiguelTremor is a manifestation of a variety of human neurodegenerative diseases, notably Parkinson’s disease (PD), a chronic disease that affects one in 100 people over age 60 years. Recent research indicates that more than five million worldwide have PD. This disease is primarily caused by a progressive loss of dopamine neurons in the nigrostriatal system that leads to widespread motor symptoms such as bradykinesia, rigidity, tremor and postural instability. Although the diagnosis of PD remains clinical, advances in functional and structural imaging have improved the ability to differentiate between PD and Essential Tremor (ET), as well as between different akinetic-rigid syndromes. No definitive test or biomarker is available for PD, so the rate of misdiagnosis is relatively high. It is therefore crucial to be able to characterize tremor in PD and ET as it is a very common feature at the onset of both diseases. This is made possible with a combination of a neuroscientific and methodological multi-modal imaging approaches, namely kinetic recording methods using accelerometers to quantify tremor amplitude and frequency and functional magnetic resonance imaging (fMRI). These allow the identification of the neural underpinnings of tremor in both PD and ET patients, which in fact have been surprisingly difficult to decipher. In this work we aim to find which tasks involving upper limb movements are suitable to modulate both PD and ET tremor. The same tasks are considered with and without added loading. The resulting analysis will allow designing an efficient fMRI protocol aiming at the identification of the cortical circuits responsible for the modulation of tremor.