Predicción de la resiliencia docente mediante redes neuronales artificiales: influencia del burnout y del estrés por COVID-19

Antecedentes: La resiliencia en el profesorado permite afrontar situaciones difíciles y reponerse a la adversidad existiendo diferencias de género al respecto. Asimismo, la inteligencia artificial y las técnicas asociadas a ella han resultado ser de gran utilidad para predecir variables educativas y estudiar la interconexión entre ellas tras la COVID19. Dicho esto, el objetivo general de esta investigación fue predecir los niveles de resiliencia en las profesoras y profesores de Secundaria a través del diseño de una red neuronal artificial (RNA). Método: Se administró la Escala Breve de Afrontamiento Resiliente, el Inventario de Burnout de Maslach y el Cuestionario de Estrés frente a la COVID19 a 401 docentes de secundaria (70.6% mujeres) de centros educativos del sureste español, con una edad media de 44.36 años (DT= 9.38). Resultados: Se hallaron diferencias en la configuración de los modelos predictivos de la resiliencia entre profesoras y profesores contribuyendo las variables independientes en diferente grado en función del género. Conclusiones: Se pone de manifiesto la utilidad de las RNA en el ámbito educativo y la necesidad de diseñar programas más ajustados.

Four reasons for adopting a life course approach to health in the COVID-19 era and beyond.

The life course approach effectively responds to pressing health needs and fills critical gaps to improve health outcomes in the era of COVID-19 and beyond. This article outlines four main reasons to adopt and implement the life course approach in public health at national and local levels: (i) the approach effectively responds to new health trends and evidence, (ii) it fills longstanding gaps in care, (iii) it best addresses health inequities, and (iv) it can help achieve more with less.

El enfoque del curso de vida da respuestas eficaces a las urgentes necesidades de salud y salva brechas críticas para mejorar los resultados de salud en la era de la COVID-19 y los años posteriores. En este artículo se describen las cuatro razones principales para adoptar y poner en práctica el enfoque del curso de vida en la salud pública a nivel local y nacional: este enfoque (i) da respuestas eficaces a las tendencias y la evidencia nuevas en el ámbito de la salud, (ii) salva brechas de larga data en la atención, (iii) aborda de la mejor manera posible las inequidades de salud, y (iv) puede contribuir a lograr más con menos recursos.

A abordagem de curso de vida responde de forma efetiva a necessidades urgentes de saúde e preenche lacunas críticas para melhorar os resultados de saúde na era da COVID-19 e mais além. Este artigo descreve quatro motivos principais para adotar e implementar a abordagem de curso de vida na saúde pública em nível nacional e local: (i) a abordagem responde de forma efetiva a novas evidências e tendências em saúde, (ii) preenche lacunas antigas nos cuidados de saúde, (iii) lida melhor com iniquidades em saúde e (iv) pode ajudar a conquistar mais com menos recursos.

Staying proper with your personal protective equipment: How to don and doff.

INTRODUCTION: The global COVID-19 pandemic highlighted the importance of protecting frontline healthcare workers from novel respiratory infections while also exposing the limited instruction that medical students receive on proper donning of personal protective equipment (PPE) and more importantly the safe doffing of contaminated PPE to minimize their risk of nosocomial infection. The best methods of providing this kind of instruction have not yet been determined. METHODS: Anesthesiology interns and CA-1 residents were trained on proper PPE donning and doffing for AGPs using a methodology based on Miller's pyramid and following a "knows-knows how-shows-does" progression. Participants donned PPE without instruction and were sprayed with Glo Germ® to identify areas of contamination, after which they received both video and in-person instruction on best practices for donning and doffing PPE for AGPs. Following instruction, they again donned PPE and were sprayed with Glo Germ® to identify areas of contamination. RESULTS: 54 participants completed the study. Before training, overall donning compliance was 60% and overall doffing compliance was 48%. Overall, 70% were contaminated after PPE doffing, with 46% having multiple sites of contamination. After training, donning compliance increased by nearly 30% (P < 0.001), doffing compliance increased by over 20% (P < 0.001), and overall contamination decreased by nearly 30% (P = 0.029), with multiple-site contamination decreasing to only 6% (P = 0.013). DISCUSSION: While best methods for providing instruction regarding topics such as PPE donning and doffing have not yet been determined, we have demonstrated that the underlying knowledge base from medical school regarding proper donning and doffing for respiratory isolation is insufficient for preventing self-contamination, and that Miller's pyramid-based training using both video and in-person instruction combined with task execution by learners can improve compliance with PPE donning and doffing protocols and more importantly decrease skin contamination among a group of early training anesthesiology residents.

Predicting teacher resilience by using artificial neural networks: influence of burnout and stress by COVID-19

Background: Resilience in teachers allows them to face difficult situations to recover from adversity and there are gender differences. Likewise, artificial intelligence and the techniques associated with it have proven to be very useful in predicting educational variables and studying the interconnection between them after COVID-19. That said, the general objective of this research was to predict the levels of resilience in secondary school teachers through the design of an artificial neural network (ANN). Method: The Brief Resilient Coping Scale, the Maslach Burnout Inventory and the COVID-19 Stress Questionnaire were administered to 401 secondary school teachers (70.6% female) from schools in southeastern Spain, with a mean age of 44.36 years (SD = 9.38). Results: Differences were found in the configuration of the predictive models of resilience between male and female teachers, with the independent variables contrib-uting to different degrees depending on gender. Conclusions: It is highlighted the usefulness of ANNs in the educational setting and the need to design more adjusted programs. © 2023: Editum. Universidad de Murcia (Spain).

Emotional Strengths and Difficulties in Italian Adolescents: Analysis of Adaptation through the SDQ

An adolescent’s knowledge of him/herself (positive aspects and weaknesses) is shaped by interactions with external and internal factors, including the family context and the educational environment. The assessment of this knowledge serves to construct the concept of self-esteem. The main objective of the present study was to examine the prevalence of self-reported emotional and behavioral symptoms in a representative sample of Italian adolescents and their relationship with gender and age, using Goodman’s Strengths and Difficulties Questionnaire (SDQ). A total of 440 adolescents (49.8% female) aged 15–18 years from the southern Italy region participated. The results show the prevalence of emotional and behavioral symptoms in the analyzed sample. Gender differences were also found in the variables under study. In conclusion, the SDQ test is offered as an agile, simplified, and effective tool, to be proposed to all formative agents who are concerned with the interest and care not only of young people, but also of the future of socio-political realities.

Stroke-derived neutrophils demonstrate higher formation potential and impaired resolution of CD66b + driven neutrophil extracellular traps.

BACKGROUND: Recent evidence suggests a merging role of immunothrombosis in the formation of arterial thrombosis. Our study aims to investigate its relevance in stroke patients. METHODS: We compared the peripheral immunological profile of stroke patients vs. healthy controls. Serum samples were functionally analyzed for their formation and clearance of Neutrophil-Extracellular-Traps. The composition of retrieved thrombi has been immunologically analyzed. RESULTS: Peripheral blood of stroke patients showed significantly elevated levels of DNAse-I (p < 0.001), LDG (p = 0.003), CD4 (p = 0.005) as well as the pro-inflammatory cytokines IL-17 (p < 0.001), INF-γ (p < 0.001) and IL-22 (p < 0.001) compared to controls, reflecting a TH1/TH17 response. Increased counts of DNAse-I in sera (p = 0.045) and Neutrophil-Extracellular-Traps in thrombi (p = 0.032) have been observed in patients with onset time of symptoms longer than 4,5 h. Lower values of CD66b in thrombi were independently associated with greater improvement of NIHSS after mechanical thrombectomy (p = 0.045). Stroke-derived neutrophils show higher potential for Neutrophil-Extracellular-Traps formation after stimulation and worse resolution under DNAse-I treatment compared to neutrophils derived from healthy individuals. CONCLUSIONS: Our data provide new insight in the role of activated neutrophils and Neutrophil-Extracellular-Traps in ischemic stroke. Future larger studies are warranted to further investigate the role of immunothrombosis in the cascades of stroke. TRIAL REGISTRATION: DRKS, DRKS00013278, Registered 15 November 2017, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013278.

ADAM17/MMP inhibition prevents neutrophilia and lung injury in a mouse model of COVID-19.

Severe coronavirus disease 2019 (COVID-19) is characterized by lung injury, cytokine storm, and increased neutrophil-to-lymphocyte ratio (NLR). Current therapies focus on reducing viral replication and inflammatory responses, but no specific treatment exists to prevent the development of severe COVID-19 in infected individuals. Angiotensin-converting enzyme-2 (ACE2) is the receptor for SARS-CoV-2, the virus causing COVID-19, but it is also critical for maintaining the correct functionality of lung epithelium and endothelium. Coronaviruses induce activation of a disintegrin and metalloprotease 17 (ADAM17) and shedding of ACE2 from the cell surface resulting in exacerbated inflammatory responses. Thus, we hypothesized that ADAM17 inhibition ameliorates COVID-19-related lung inflammation. We employed a preclinical mouse model using intratracheal instillation of a combination of polyinosinic:polycytidylic acid (poly(I:C)) and the receptor-binding domain of the SARS-CoV-2 spike protein (RBD-S) to mimic lung damage associated with COVID-19. Histologic analysis of inflamed mice confirmed the expected signs of lung injury including edema, fibrosis, vascular congestion, and leukocyte infiltration. Moreover, inflamed mice also showed an increased NLR as observed in critically ill COVID-19 patients. Administration of the ADAM17/MMP inhibitors apratastat and TMI-1 significantly improved lung histology and prevented leukocyte infiltration. Reduced leukocyte recruitment could be explained by reduced production of proinflammatory cytokines and lower levels of the endothelial adhesion molecules ICAM-1 and VCAM-1. Additionally, the NLR was significantly reduced by ADAM17/MMP inhibition. Thus, we propose inhibition of ADAM17/MMP as a novel promising treatment strategy in SARS-CoV-2-infected individuals to prevent the progression toward severe COVID-19.

Burnout, Resilience, and COVID-19 among Teachers: Predictive Capacity of an Artificial Neural Network

Emotional exhaustion, cynicism, and work inefficiency are three dimensions that define burnout syndrome among teachers. On another note, resilience can be understood as the ability to adapt to the environment and overcome adverse situations. In addition, COVID-19 has provided a threatening environment that has led to the implementation of resilience strategies to struggle with burnout and cope with the virus. The aim of this study was to analyze the relationship between resilience, burnout dimensions, and variables associated with COVID-19 through the design of an artificial neural network architecture. For this purpose, the Maslach Burnout Inventory-General Survey (MBI-GS), the Brief Resilience Coping Scale (BRCS), and a questionnaire on stress towards COVID-19 were administered to 419 teachers from secondary schools in southeastern Spain (292 females;69.7%). The results showed that 30.8% suffered from burnout (high emotional exhaustion, high cynicism, and low professional efficacy) and that 38.7% had a high level of resilience, with an inverse relationship between both constructs. Likewise, we modelled an ANN able to predict burnout syndrome among 97.4% of teachers based on its dimensions, resilience, sociodemographic variables, and the stress generated by COVID-19. Our conclusions shed some light on the efficacy of relying on artificial intelligence in the educational field to predict the psychological situation of teachers and take early action.

Estimation of all-cause excess mortality by age-specific mortality patterns for countries with incomplete vital statistics: a population-based study of the case of Peru during the first wave of the COVID-19 pandemic.

BACKGROUND: All-cause excess mortality is a comprehensive measure of the combined direct and indirect effects of COVID-19 on mortality. Estimates are usually derived from Civil Registration and Vital Statistics (CRVS) systems, but these do not include non-registered deaths, which may be affected by changes in vital registration coverage over time. METHODS: Our analytical framework and empirical strategy account for registered mortality and under-registration. This provides a better estimate of the actual mortality impact of the first wave of the COVID-19 pandemic in Peru. We use population and crude mortality rate projections from Peru's National Institute of Statistics and Information (INEI, in Spanish), individual-level registered COVID-19 deaths from the Ministry of Health (MoH), and individual-level registered deaths by region and age since 2017 from the National Electronic Deaths Register (SINADEF, in Spanish).We develop a novel framework combining different estimates and using quasi-Poisson models to estimate total excess mortality across regions and age groups. Also, we use logistic mixed-effects models to estimate the coverage of the new SINADEF system. FINDINGS: We estimate that registered mortality underestimates national mortality by 37•1% (95% CI 23% - 48•5%) across 26 regions and nine age groups. We estimate total all-cause excess mortality during the period of analysis at 173,099 (95% CI 153,669 - 187,488) of which 108,943 (95% CI 96,507 - 118,261) were captured by the vital registration system. Deaths at age 60 and over accounted for 74•1% (95% CI 73•9% - 74•7%) of total excess deaths, and there were fewer deaths than expected in younger age groups. Lima region, on the Pacific coast and including the national capital, accounts for the highest share of excess deaths, 87,781 (95% CI 82,294 - 92,504), while in the opposite side regions of Apurimac and Huancavelica account for less than 300 excess deaths. INTERPRETATION: Estimating excess mortality in low- and middle-income countries (LMICs) such as Peru must take under-registration of mortality into account. Combining demographic trends with data from administrative registries reduces uncertainty and measurement errors. In countries like Peru, this is likely to produce significantly higher estimates of excess mortality than studies that do not take these effects into account. FUNDING: None.

Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment.

The COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people (73% women) from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences (version 24) was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic (before and after the implementation of isolation measures), with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community.