Acosta Pérez, Paul Bladimir
Thumbnail Image
Preferred name
Acosta Pérez, Paul Bladimir
Main Affiliation
Ambato
Email
paulacosta@uti.edu.ec
ORCID
Scopus Author ID
58759036200

Research Output

Filters
Reset filters

Settings
Now showing 1 - 4 of 4
No Thumbnail Available
Publication

Occupational Risks: A Comparative Study of the Most Common Indicators in Uruguay, Cuba and Ecuador

2023 , Acosta Pérez, Paul Bladimir , Espinosa Pinos, Carlos Alberto , Acuña Mayorga, José Miguel , Lascano Arias, Giovanni

Efficiency and effectiveness in daily work activities demand the control of processes, those elements that can affect the health of employees known as occupational risks. The objective of this study was to identify indicators of more frequent labor risks in the countries of Uruguay, Cuba and Ecuador, for which a bibliographic compilation was carried out, as well as a descriptive analysis of the indicators of occupational risk. The results show that the countries analyzed coincide as the highest index of risks to manufacturing industries. It is concluded that the international regulations and conventions that govern safety have been accepted by the different countries that make them up, mainly in the statistical registry of accidents, reports, affiliates among others. Finally, policies aimed at the prevention, detection, monitoring and eradication of occupational risks in the workplace must be established. © 2023 IEEE.

View
No Thumbnail Available
Publication

Bibliometric Analysis of Mental Health Research in Populations Affected by Natural Disasters

2023 , Espinosa Pinos, Carlos Alberto , Lascano-Arias G.S. , Acosta Pérez, Paul Bladimir , Acuña Mayorga, José Miguel

The number of climate-related disasters has tripled over the past 30 years, culminating in the last two periods during catastrophic climate disasters worldwide such as Cyclone Idai, deadly heat waves in India, Pakistan, and Europe; and floods in Southeast Asia. Because natural disasters can have severe consequences for affected people's mental health, this study aims to identify trends and patterns in scientific production related to the mental health of people affected by natural risks. Five hundred thirty-two relevant articles were initially identified from the Scopus database in February 2023. Based on descriptive results, the number of scientific publications increased steadily from 2019 to 2022, albeit slowly in recent years, with the main publication form being articles, followed by articles, abstracts, and book chapters, conference papers, editorials, memos, books, letters, and short surveys. Among the fields, medicine had the most articles, followed by social sciences, psychology, environmental sciences, earth and planetary sciences, engineering, nursing, computer sciences, arts and humanity, neurosciences, business, economics, energy, chemistry, biology, and health. Co-occurrence analysis of terms of titles and abstracts identified four themes: 1) impact of natural disasters on mental health and COVID-19 and risk management; 2) the effect of forest fires on the mental health of the affected population; 3) earthquakes and tsunamis affect the mental health of the affected population; and 4) resilience and social support in psychological adjustment during pregnancy. A longitudinal analysis based on titles and abstracts showed how the focus shifted from initial associations between natural disasters and the physical and mental health of survivors (in 2018) to the association between natural disasters and cardiovascular disease and traumatic experiences and postpartum depressive symptoms (at the beginning 2023). This study concludes by discussing the theoretical and practical implications of the findings and showing how an area of particular interest for future research is the study of community mental health resilience as an intervention strategy to mitigate the negative effects of natural disasters in disaster-affected communities. © 2023 IEEE.

View
No Thumbnail Available
Publication

Unraveling Psycholaboral Risk Factors: Ordinal Prediction of Teacher Well-Being in University Institutions in Ecuador

2025 , Espinosa Pinos, Carlos Alberto , Acosta Pérez, Paul Bladimir , Valarezo Calero, Camila

This article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) were stratifiedly selected, resulting in a total of 1600 data collected through online forms. The research was of cross-sectional design and quantitative type using an ordinal logistic regression model, analyzing variables such as ethnic identity, field of knowledge, gender, number of children, job burnout, perceived stress and occupational risk. The results indicate that teachers belonging to ethnic groups such as Ethnicity White, Mestizo and Montubio have higher job satisfaction probabilities of 96.7%, 92.3% and 115.3%, respectively. In addition, teachers in Humanities and Communication and Information Sciences report higher job satisfaction, with increases of 53.7% and 55.6%. In contrast, those who identify as “Other” in terms of gender experience a 21.2% decrease in satisfaction. Each additional child is associated with a 21.2% decrease in job satisfaction, while an increase in Work-Related Burnout, Perceived Stress, and Occupational Risk is associated with decreases of 27.3%, 16.2%, and 31.4%, respectively. These findings highlight the need for inclusive policies and effective strategies to improve teacher well-being in the academic university environment

View
No Thumbnail Available
Publication

Applying Classification Techniques in Machine Learning to Predict Job Satisfaction of University Professors: A Sociodemographic and Occupational Perspective

2024 , Espinosa Pinos, Carlos Alberto , Acosta Pérez, Paul Bladimir , Camila Alessandra Valarezo-Calero

This article investigates the factors that affect the job satisfaction of university teachers for which 400 teachers from 4 institutions (public and private) in Ecuador were stratified, resulting in a total of 1600 data points collected through online forms. The research was of a cross-sectional design and quantitative and used machine learning techniques of classification and prediction to analyze variables such as ethnic identity, field of knowledge, gender, number of children, job burnout, perceived stress, and occupational risk. The results indicate that the best classification model is neural networks with a precision of 0.7304; the most significant variables for predicting the job satisfaction of university teachers are: the number of children they have, scores related to perceived stress, professional risk, and burnout, province of the university at which the university teacher surveyed works, and city where the teacher works. This is in contrast to marital status, which does not contribute to its prediction. These findings highlight the need for inclusive policies and effective strategies to improve teacher well-being in the university academic environment.

View