Propriedades psicométricas e invariância de medida de uma escala de autoeficácia acadêmica em estudantes universitários de cinco países da América Latina

Our objective was to analyze the measurement invariance of the Academic Situations Specific Perceived Self-Efficacy Scale (ASSPSES) among Peruvian, Mexican, Colombian, Argentinean, and Brazilian college students, and its relationship with academic emotional exhaustion (AEE). In addition to the ASSPSES, the Emotional Exhaustion Scale (EES) was used. Two thousand one hundred forty-two college students (70.26% women) between the ages of 16 and 35 (M = 21.79 years) participated. The one-dimensional structure of ASSPSES was corroborated in each sample with a confirmatory factor analysis and the measurement invariance analysis was carried out with a multi-group factor analysis (MGFA) and with the differential item functioning. The relationship with the AEE was tested with the Pearson correlation coefficient. Regard the results, in all of the samples, the one-dimensional model presented adequate psychometric indicators with respect to both dimensionality and reliability. Similarly, regarding the analysis of measurement invariance, a strong variance was attained, and DIF is weak, which together with the MGFA results provides favorable evidence of measurement invariance. We conclude that ASSPSES is an invariant measure among the analyzed groups although replicating the study is necessary for the consolidation of the findings. These findings contribute to the understanding of the internal structure of the ASSPSES, something that had been awaiting evaluation, given how the scale is used in various contexts.El objetivo fue analizar la invarianza de medición de la Escala de Autoeficacia Percibida Específica para Situaciones Académicas (EAPESA) entre estudiantes universitarios peruanos, mexicanos, colombianos, argentinos y brasileños, y su relación con el agotamiento emocional académico (AEA). Además de la EAPESA, se usó la Escala de Cansancio Emocional (ECE). Participaron 2142 estudiantes universitarios (70.26 % mujeres) entre los 16 y 35 años (M = 21.79). La estructura unidimensional de la EAPESA se corroboró en cada muestra con un análisis factorial confirmatorio, y la invarianza de medición se llevó a cabo con un análisis factorial de grupo múltiple (AFGM) y con el funcionamiento diferencial de los ítems (FDI). La relación con el AEA se evaluó con el coeficiente de correlación de Pearson. Respecto a los resultados, en todas las muestras el modelo unidimensional presentó adecuados indicadores psicométricos, tanto en lo que respecta a su dimensionalidad como en cuanto a su confiabilidad. Del mismo modo, en cuanto al análisis de invarianza de medición, se alcanza la invarianza fuerte, y el FDI es débil, lo que junto con el AFGM brinda evidencia favorable de invarianza de medición. Se concluye que la EAPESA es una medida invariante entre los grupos analizados, aunque es necesario replicar la investigación para consolidar los hallazgos. Estos resultados contribuyen a entender la estructura interna de la EAPESA, algo que estaba aguardando una valoración, ya que dicha escala es usada en varios contextos.El objetivo fue analizar la invarianza de medición de la Escala de Autoeficacia Percibida Específica para Situaciones Académicas (EAPESA) entre estudiantes universitarios peruanos, mexicanos, colombianos, argentinos y brasileños, y su relación con el agotamiento emocional académico (AEA). Además de la EAPESA, se usó la Escala de Cansancio Emocional (ECE). Participaron 2142 estudiantes universitarios (70.26% mujeres) entre los 16 y 35 años (M = 21.79). La estructura unidimensional de la EAPESA se corroboró en cada muestra con un análisis factorial confirmatorio, y la invarianza de medición se llevó a cabo con un análisis factorial de grupo múltiple (AFGM) y con el funcionamiento diferencial de los ítems (FDI). La relación con el AEA se evaluó con el coeficiente de correlación de Pearson. Respecto a los resultados, en todas las muestras el modelo unidimensional presentó adecuados indicadores psicométricos, tanto en lo que respecta a su dimensionalidad como en cuanto a su confiabilidad. Del mismo modo, en cuanto al análisis de invarianza de medición, se alcanza la invarianza fuerte, y el FDI es débil, lo que junto con el AFGM brinda evidencia favorable de invarianza de medición. Se concluye que la EAPESA es una medida invariante entre los grupos analizados, aunque es necesario replicar la investigación para consolidar los hallazgos. Estos resultados contribuyen a entender la estructura interna de la EAPESA, algo que estaba aguardando una valoración, ya que dicha escala es usada en varios contextos

Developing Strategies for Onchocerciasis Elimination Mapping and Surveillance Through The Diagnostic Network Optimization Approach

Background Onchocerciasis (river blindness) is a filarial disease targeted for elimination of transmission. However, challenges exist to the implementation of effective diagnostic and surveillance strategies at various stages of elimination programs. To address these challenges, we used a network data analytics approach to identify optimal diagnostic scenarios for onchocerciasis elimination mapping (OEM). Methods The diagnostic network optimization (DNO) method was used to model the implementation of the old Ov16 rapid diagnostic test (RDT) and of new RDTs in development for OEM under different testing strategy scenarios with varying testing locations, test performance and disease prevalence. Environmental suitability scores (ESS) based on machine learning algorithms were developed to identify areas at risk of transmission and used to select sites for OEM in Bandundu region in the Democratic Republic of Congo (DRC) and Uige province in Angola. Test sensitivity and specificity ranges were obtained from the literature for the existing RDT, and from characteristics defined in the target product profile for the new RDTs. Sourcing and transportation policies were defined, and costing information was obtained from onchocerciasis programs. Various scenarios were created to test various state configurations. The actual demand scenarios represented the disease prevalence at IUs according to the ESS, while the counterfactual scenarios (conducted only in the DRC) are based on adapted prevalence estimates to generate prevalence close to the statistical decision thresholds (5% and 2%), to account for variability in field observations. The number of correctly classified implementation units (IUs) per scenario were estimated and key cost drivers were identified. Results In both Bandundu and Uige, the sites selected based on ESS had high predicted onchocerciasis prevalence >10%. Thus, in the actual demand scenarios in both Bandundu and Uige, the old Ov16 RDT correctly classified all 13 and 11 IUs, respectively, as requiring CDTi. In the counterfactual scenarios in Bandundu, the new RDTs with higher specificity correctly classified IUs more cost effectively. The new RDT with highest specificity (99.8%) correctly classified all 13 IUs. However, very high specificity (e.g., 99.8%) when coupled with imperfect sensitivity, can result in many false negative results (missing decisions to start MDA) at the 5% statistical decision threshold (the decision rule to start MDA). This effect can be negated by reducing the statistical decision threshold to 2%. Across all scenarios, the need for second stage sampling significantly drove program costs upwards. The best performing testing strategies with new RDTs were more expensive than testing with existing tests due to need for second stage sampling, but this was offset by the cost of incorrect classification of IUs. Conclusion The new RDTs modelled added most value in areas with variable disease prevalence, with most benefit in IUs that are near the statistical decision thresholds. Based on the evaluations in this study, DNO could be used to guide the development of new RDTs based on defined sensitivities and specificities. While test sensitivity is a minor driver of whether an IU is identified as positive, higher specificities are essential. Further, these models could be used to explore the development and optimization of new tools for other neglected tropical diseases

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Psychometric properties and measurement invariance of an academic self-efficacy scale in college students from five Latin American countries

Our objective was to analyze the measurement invariance of the Academic Situations Specific Perceived Self-Efficacy Scale (ASSPSES) among Peruvian, Mexican, Colombian, Argentinean, and Brazilian college students, and its relationship with academic emotional exhaustion (AEE). In addition to the ASSPSES, the Emotional Exhaustion Scale (EES) was used. Two thousand one hundred forty-two college students (70.26% women) between the ages of 16 and 35 (M = 21.79 years) participated. The one-dimensional structure of ASSPSES was corroborated in each sample with a confirmatory factor analysis and the measurement invariance analysis was carried out with a multi-group factor analysis (MGFA) and with the differential item functioning. The relationship with the AEE was tested with the Pearson correlation coefficient. Regard the results, in all of the samples, the one-dimensional model presented adequate psychometric indicators with respect to both dimensionality and reliability. Similarly, regarding the analysis of measurement invariance, a strong variance was attained, and DIF is weak, which together with the MGFA results provides favorable evidence of measurement invariance. We conclude that ASSPSES is an invariant measure among the analyzed groups although replicating the study is necessary for the consolidation of the findings. These findings contribute to the understanding of the internal structure of the ASSPSES, something that had been awaiting evaluation, given how the scale is used in various contexts.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000168041https://orcid.org/0000-0002-5858-4964https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000004241william.tamayoa@campusucc.edu.cohttps://scholar.google.es/citations?user=RBbJI3sAAAAJ&hl=e

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data