Role of Empathy and Lifelong Learning Abilities in Physicians and Nurses Who Work in Direct Contact with Patients in Adverse Working Conditions
Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Artículos y libros Abierto Inglés Empathy and lifelong learning are two professional competencies that depend on the four principles of professionalism: humanism, altruism, excellence, and accountability. In occupational health, there is evidence that empathy prevents work distress. However, in the case of lifelong learning, the evidence is still scarce. In addition, recent studies suggest that the development of lifelong learning varies in physicians and nurses and that it is sensitive to the influence of cultural stereotypes associated with professional roles. This study was performed with the purpose of determining the specific role that empathy and lifelong learning play in the reduction in occupational stress. This study included a sample composed by 40 physicians and 40 nurses with high dedication to clinical work in ambulatory consultations from a public healthcare institution in Paraguay. Somatization, exhaustion, and work alienation, described as indicators of occupational stress, were used as dependent variables, whereas empathy, lifelong learning, gender, discipline, professional experience, civil status, and family burden were used as potential predictors. Three multiple regression models explained 32% of the variability of somatization based on a linear relationship with empathy, lifelong learning, and civil status; 73% of the variability of exhaustion based on a linear relationship with empathy, somatization, work alienation, and discipline; and 62% of the variability of work alienation based on a linear relationship with lifelong learning, exhaustion, and discipline. These findings indicate that empathy and lifelong learning play important roles in the prevention of work distress in physicians and nurses. However, this role varies by discipline. metadata Delgado Bolton, Roberto C.; San-Martín, Montserrat y Vivanco, Luis mail SIN ESPECIFICAR, SIN ESPECIFICAR, luis.vivanco@uneatlantico.es (2022) Role of Empathy and Lifelong Learning Abilities in Physicians and Nurses Who Work in Direct Contact with Patients in Adverse Working Conditions. International Journal of Environmental Research and Public Health, 19 (5). p. 3012. ISSN 1660-4601
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Empathy and lifelong learning are two professional competencies that depend on the four principles of professionalism: humanism, altruism, excellence, and accountability. In occupational health, there is evidence that empathy prevents work distress. However, in the case of lifelong learning, the evidence is still scarce. In addition, recent studies suggest that the development of lifelong learning varies in physicians and nurses and that it is sensitive to the influence of cultural stereotypes associated with professional roles. This study was performed with the purpose of determining the specific role that empathy and lifelong learning play in the reduction in occupational stress. This study included a sample composed by 40 physicians and 40 nurses with high dedication to clinical work in ambulatory consultations from a public healthcare institution in Paraguay. Somatization, exhaustion, and work alienation, described as indicators of occupational stress, were used as dependent variables, whereas empathy, lifelong learning, gender, discipline, professional experience, civil status, and family burden were used as potential predictors. Three multiple regression models explained 32% of the variability of somatization based on a linear relationship with empathy, lifelong learning, and civil status; 73% of the variability of exhaustion based on a linear relationship with empathy, somatization, work alienation, and discipline; and 62% of the variability of work alienation based on a linear relationship with lifelong learning, exhaustion, and discipline. These findings indicate that empathy and lifelong learning play important roles in the prevention of work distress in physicians and nurses. However, this role varies by discipline.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | somatization; exhaustion; work alienation; empathy; lifelong learning; nursing; medicine |
| Clasificación temática: | Materias > Biomedicina |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros |
| Depositado: | 03 Oct 2022 12:41 |
| Ultima Modificación: | 17 Jul 2023 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/3741 |
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Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
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A novel machine learning-based proposal for early prediction of endometriosis disease
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Background The aging process leads to negative changes in various bodily systems, including the neuromuscular system. Strength training, is considered the best strategy to counteract these neuromuscular changes, preventing sarcopenia and frailty in older adults. Objective To compare the effects of strength training with elastic resistance and free weights on the muscle strength of knee extensors and flexors and functional performance in the older adults. Methods This was a randomised clinical study. Thirty-one participants of both sexes were allocated randomly into two groups: Training Group Free Weight (TGFW, n = 15) and Training Group with Elastic Resistance (TGER, n = 16). Two individuals were excluded and so, twenty-nine individuals were evaluated before and after eight weeks training protocol, which was performed three times a week. The determination of the training load was obtained using a protocol of 10 repetitions maximum. Results No significant differences were found in either the intra- or the inter-group comparisons, on functional performance and peak muscle strength. In the intra-groups (pre- and post-strength training), it was observed that both groups significantly increased the training load (10 RM) for the extensors (TGFW p = 0.0002; TGER p = 0.0001) and the knee flexors (TGFW p = 0.006; TGER p = 0.0001). Conclusion Both training protocols similarly were effective in increasing the training load observed by the 10 RM test of the extension and flexion movements of the knee.
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In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.
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