Modelo curricular para la integración de principios éticos del Código Deontológico Internacional en la Licenciatura en Enfermería
Tesis Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Español Siendo la ética un elemento señalado como imprescindible para la enferme-ría como ciencia, se analizó la integración de principios y valores éticos en las 11 escuelas activas de enfermería en República Dominicana, desde agosto 2013 hasta junio 2018, con el propósito de crear un modelo curricular para integrar principios éticos en la carrera, y garantizar un profesional comprometido y competente en brindar cuidados humanizados. El diagnóstico base del modelo utilizó una me-todología mixta por componentes, con análisis de contenido a documentos oficia-les de la carrera y a declaraciones ofrecidas por los administradores de las escuelas en grupo nominal, y con aplicación de encuestas a docentes (91) y estudiantes (417), más un test a egresados (203). El análisis de datos usó el SPSS y el Atlas.ti. Los resultados mostraron que la escala de valores de los egresados no tiene relación estadística significativa con la derivación coherente de la escuela que los forma. Los estudiantes dicen que su escuela hace promoción de principios y valores pero no es efectiva (86%), pues, falta modelaje en el personal (78%) y no se valora fuera del aula (76%) ni en la evaluación al docente (79%). Los docentes promueven principios con apego al prontuario, en el cual, la integración es débil o ausente. Se concluye que la formación ética de enfermeros requiere definición precisa de un enfoque integrador de referentes éticos al currículo, concepción y gestión del docente, concepción y acompañamiento del estudiante, disposición de recursos didácticos, mejor infraestructura funcional y estrategias de asegura-miento de la construcción del perfil del egresado. Con estos parámetros se desarrolló un modelo que se ha denominado deontovalidación como metodología y proceso de integración sistemática de principios éticos al currículo de enfermería. El modelo se está implementando en una escuela y se da capacitación al personal de otras dos. metadata Suero Moreta, Alfa Rigel mail arsuerom@gmail.com (2025) Modelo curricular para la integración de principios éticos del Código Deontológico Internacional en la Licenciatura en Enfermería. Doctoral thesis, Universidad Internacional Iberoamericana México.
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Siendo la ética un elemento señalado como imprescindible para la enferme-ría como ciencia, se analizó la integración de principios y valores éticos en las 11 escuelas activas de enfermería en República Dominicana, desde agosto 2013 hasta junio 2018, con el propósito de crear un modelo curricular para integrar principios éticos en la carrera, y garantizar un profesional comprometido y competente en brindar cuidados humanizados. El diagnóstico base del modelo utilizó una me-todología mixta por componentes, con análisis de contenido a documentos oficia-les de la carrera y a declaraciones ofrecidas por los administradores de las escuelas en grupo nominal, y con aplicación de encuestas a docentes (91) y estudiantes (417), más un test a egresados (203). El análisis de datos usó el SPSS y el Atlas.ti. Los resultados mostraron que la escala de valores de los egresados no tiene relación estadística significativa con la derivación coherente de la escuela que los forma. Los estudiantes dicen que su escuela hace promoción de principios y valores pero no es efectiva (86%), pues, falta modelaje en el personal (78%) y no se valora fuera del aula (76%) ni en la evaluación al docente (79%). Los docentes promueven principios con apego al prontuario, en el cual, la integración es débil o ausente. Se concluye que la formación ética de enfermeros requiere definición precisa de un enfoque integrador de referentes éticos al currículo, concepción y gestión del docente, concepción y acompañamiento del estudiante, disposición de recursos didácticos, mejor infraestructura funcional y estrategias de asegura-miento de la construcción del perfil del egresado. Con estos parámetros se desarrolló un modelo que se ha denominado deontovalidación como metodología y proceso de integración sistemática de principios éticos al currículo de enfermería. El modelo se está implementando en una escuela y se da capacitación al personal de otras dos.
| Tipo de Documento: | Tesis (Doctoral) |
|---|---|
| Palabras Clave: | ética de enfermería, modelo curricular, derivación y coherencia, principios éticos, código deontológico de enfermería |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales |
| Depositado: | 28 May 2025 23:30 |
| Ultima Modificación: | 28 May 2025 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/14627 |
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Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.
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A scalable and secure federated learning authentication scheme for IoT
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Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models
Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.
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Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.
Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,
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