Medición de la resiliencia en adultos víctimas del conflicto: Caso Carmen de Bolívar, Colombia
Artículo Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Español El presente artículo es resultado de una investigación que tuvo por objetivo medir la resiliencia en adultos víctimas del conflicto en el Carmen de Bolívar, Colombia. Para ello, se aplicó un cuestionario estructurado de breve duración, de 32 reactivos donde se evalúan las variables factores protectores internos, factores protectores externos y Empatía. Participaron 231 personas adultas del área geográfica estudiada. Entre los resultados se encontró que entre los factores protectores interno destaca la confianza en si mismos, consideran que la educación es muy importante entre los factores protectores externos y con respecto a la empatía confían en un mejor mañana. Se concluyó que, si bien es limitada la definición y medición de la resiliencia por la naturaleza dinámica y los múltiples factores de influencia del constructo, la población civil encuestada evidencia elementos característicos de la conducta resiliente posterior a haber sido afectados por el conflicto armado en su región. metadata Candanoza-Henríquez, Alberto Enrique y Rojo Gutiérrez, Marco Antonio mail SIN ESPECIFICAR, marco.rojo@unini.edu.mx (2021) Medición de la resiliencia en adultos víctimas del conflicto: Caso Carmen de Bolívar, Colombia. Política, Globalidad y Ciudadanía. pp. 254-280. ISSN 2395-8448
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El presente artículo es resultado de una investigación que tuvo por objetivo medir la resiliencia en adultos víctimas del conflicto en el Carmen de Bolívar, Colombia. Para ello, se aplicó un cuestionario estructurado de breve duración, de 32 reactivos donde se evalúan las variables factores protectores internos, factores protectores externos y Empatía. Participaron 231 personas adultas del área geográfica estudiada. Entre los resultados se encontró que entre los factores protectores interno destaca la confianza en si mismos, consideran que la educación es muy importante entre los factores protectores externos y con respecto a la empatía confían en un mejor mañana. Se concluyó que, si bien es limitada la definición y medición de la resiliencia por la naturaleza dinámica y los múltiples factores de influencia del constructo, la población civil encuestada evidencia elementos característicos de la conducta resiliente posterior a haber sido afectados por el conflicto armado en su región.
Tipo de Documento: | Artículo |
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Palabras Clave: | Adultos, conflicto armado, resiliencia, Carmen de Bolívar |
Clasificación temática: | Materias > Ciencias Sociales |
Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Artículos y libros |
Depositado: | 25 May 2022 23:55 |
Ultima Modificación: | 25 May 2022 23:55 |
URI: | https://repositorio.unini.edu.mx/id/eprint/2078 |
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Carotenoids Intake and Cardiovascular Prevention: A Systematic Review
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The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.
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Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.
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