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Assessing the impact of a business intelligence program on the employability and well-being of low-income women: a quasi-experimental study protocol
(Frontiers Media SA, 2025-11-13)
Marco Faytong-Haro
;
Alonso Quijano-Ruiz
;
Daniel Sanchez-Pazmiño
;
Patricio Alvarez-Muñoz
;
Jose Diaz-Montenegro
Women are underrepresented globally in the field of data analytics, particularly in underdeveloped countries. We present a protocol to assess the impact of the New Dimensions program, a data analytics and business intelligence course sequence that aims to address this gender gap by providing free business intelligence training to disadvantaged women in Ecuador. The program offers both technical (Business Intelligence) and soft skills training, including Excel, Power BI, SQL, GitHub, R, Tableau, statistics, Python, and workshops on empowerment, employability, and public speech. The purpose of this quasi-experimental study is to assess the impact of this training program on employability and other well-being outcomes of the participants. A total of 80 individuals will be part in the study, of which 70 will be selected to participate in the program, 50 will receive both hard and soft skills training, and 20 only soft skills training. Ten individuals will form part of the control group with no intervention. The study design involves a nonrandomized control group composed of rejected applicants. Data will be collected through an online application form and a computer-based exam. The outcome measures are participants' labor market outcomes, income, food security, and economic stratification, among others. This protocol will prospectively evaluate the program's potential effectiveness; findings will inform future, larger randomized studies focused on employability and well-being in underrepresented groups.
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Healthcare attitudes toward depression in Latin America: a latent class analysis from Argentina, Chile, Ecuador, Peru, and Venezuela using the Spanish-validated revised depression attitude questionnaire (SR-DAQ)
(Springer Science and Business Media LLC, 2025-10-01)
Marco Faytong-Haro
;
Genesis Camacho-Leon
;
Robert Araujo-Contreras
;
Stephanie Gallegos
;
Hans Mautong
Background: Depression significantly impacts health systems worldwide, particularly in Latin America, where cultural stigmatization and misconceptions about mental health deter individuals from seeking help. Healthcare professionals’ attitudes toward depression may affect its prevention, diagnosis and treatment. Objective: To categorize Latin American healthcare professionals’ attitudes towards diagnosis and management of depression in subgroups using the Spanish-validated Revised Depression Attitude Questionnaire (SR-DAQ). Methods: A cross-sectional study surveyed 2,409 professionals using SR-DAQ from 2019 to 2022. Latent class analysis and multinomial logistic regression were used to identify attitude classes and explore demographic influences. Results: Among our sample, four attitude classes were identified: Depression Skeptics (21%), Depression Cautious (33%), Depression Neutrals (18%), and Depression Advocates (28%). Gender and medical subspecialty significantly influenced class membership, with females and mental health specialists more likely to be part of the Advocates. Conclusion: The study reveals varied attitudes towards depression among Latin American healthcare professionals, suggesting the need for tailored public health strategies to enhance effective depression care and management.
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Effectiveness of postoperative cephalosporins in reducing urinary tract infections and other parameters following transurethral resection of the prostate: a systematic review and meta-analysis
(Elsevier BV, 2025-02-01)
Wael Hafez
;
Feras Al-Obeidat
;
Asrar Rashid
;
Arun Kumar Venkatachalapathi
;
Amr Massaod
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Association between type of health insurance and dental visits among Ecuadorian older population: evidence from a cross-sectional study
(Springer Science and Business Media LLC, 2025-01-08)
Marite Falquez
;
Adriana Canessa Rojas
;
Antonio Lanata-Flores
;
Freddy Espinoza-Carrasco
;
Ivan Cherrez-Ojeda
Background: Dental services are often overlooked within healthcare systems, despite their critical role in overall health. Socioeconomic barriers and disparities in insurance coverage frequently limit access to oral care, particularly among vulnerable populations like older adults. Objective: To examine the impact of different health insurance types on the frequency of dental consultations among Ecuadorian senior citizens, aiming to highlight gaps in access to care. Methods: Using the 2009 National Survey of Health, Well-being, and Aging (SABE), which aims to determine the health and living conditions of older individuals, this study analyzed the health insurance status and dental service visits of 4,565 older adults in Ecuador. Binary logistic regression was employed to explore the association between insurance type and dental-care utilization. Results: In the sample, 76.50% of older adults had not visited a dentist in the past year. Insurance breakdown revealed 17.04% with general IESS, 9.59% with rural IESS, 2.23% with private insurance, 1.86% with ISSFA/ISSPOL, 1.86% with municipal insurance, and 69.66% were uninsured. Individuals with private insurance or ISSFA/ISSPOL insurance were 32% and 91% more likely, respectively, to seek oral care compared to those without these respective insurances. Conversely, uninsured individuals were 43% less likely to seek dental care than those with any type of insurance. Conclusion: Health insurance significantly influences the frequency of dental consultations among Ecuadorian seniors, highlighting the need for policy interventions to ensure equitable access to oral care.
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Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis
(Frontiers Media SA, 2025-01-17)
Feras Al-Obeidat
;
Wael Hafez
;
Asrar Rashid
;
Mahir Khalil Jallo
;
Munier Gador
Background: Leukemia is the 11th most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood malignancy in adults. Microscopic blood tests are the most common methods for identifying leukemia subtypes. An automated optical image-processing system using artificial intelligence (AI) has recently been applied to facilitate clinical decision-making. Aim: To evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML). Methods: Medical databases including PubMed, Web of Science, and Scopus were searched until December 2023. We used the “metafor” and “metagen” libraries in R to analyze the different models used in the studies. Accuracy and sensitivity were the primary outcome measures. Results: Ten studies were included in our review and meta-analysis, conducted between 2016 and 2023. Most deep-learning models have been utilized, including convolutional neural networks (CNNs). The common- and random-effects models had accuracies of 1.0000 [0.9999; 1.0001] and 0.9557 [0.9312, and 0.9802], respectively. The common and random effects models had high sensitivity values of 1.0000 and 0.8581, respectively, indicating that the machine learning models in this study can accurately detect true-positive leukemia cases. Studies have shown substantial variations in accuracy and sensitivity, as shown by the Q values and I2 statistics. Conclusion: Our systematic review and meta-analysis found an overall high accuracy and sensitivity of AI models in correctly identifying true-positive AML cases.

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