Objective: To quantify the evolution, impact, and importance of normative data (ND) calculation by identifying trends in the research literature and what approaches need improvement. Methods: A PRISMA-guideline systematic review was performed on literature from 2000 to 2022 in PubMed, Pub-Psych, and Web of Science. Inclusion criteria included scientific articles about ND in neuropsychological tests with clear data analysis, published in any country, and written in English or Spanish. Cross-sectional and longitudinal studies were included. Bibliometric analysis was used to examine the growth, productivity, journal dispersion, and impact of the topic. VOSViewer compared keyword co-occurrence networks between 1952–1999 and 2000–2022. Results: Four hundred twelve articles met inclusion and exclusion criteria. The most studied predictors were age, education, and sex. There were a greater number of studies/projects focusing on adults than children. The Verbal Fluency Test (12.7%) was the most studied test, and the most frequently used variable selection strategy was linear regression (49.5%). Regression-based approaches were widely used, whereas the traditional approach was still used. ND were presented mostly in percentiles (44.2%). Bibliometrics showed exponential growth in publications. Three journals (2.41%) were in the Core Zone. VOSViewer results showed small nodes, long distances, and four ND-related topics from 1952 to 1999, and there were larger nodes with short connections from 2000 to 2022, indicating topic spread. Conclusions: Future studies should be conducted on children’s ND, and alternative statistical methods should be used over the widely used regression approaches to address limitations and support growth of the field.

Background: The Coronavirus disease (COVID-19) pandemic profoundly impacted education, causing uncertainty and constant changes for teachers, including new teaching modalities, increased workloads, and limited rest periods. Global studies have reported mental health issues such as anxiety and depression among educators during and after the pandemic, primarily in Asia, Europe, and North America. However, these existing studies often show increased anxiety and depression rather than considering school-based predictors that may contribute to increasing rates. Aims: Despite these global findings, there are few investigations of the impact of COVID-19 on teachers in Latin America and Spain. To address these notable global and regional research gaps, this study examined the prevalence and predictors of depression and anxiety symptoms among teachers in Latin America and Spain. Method: An online survey collected data on demographics, work-related characteristics, COVID-19 experiences, and measures of mental health from teachers in 19 Latin American countries and Spain using a snowball sampling approach. The sample comprised 2004 teachers, with 81.8% female participants and a mean age of 44.46 years (SD = 9.8). Descriptive statistics and hierarchical multiple linear regressions were conducted with depression and anxiety as outcome variables. Results: Consistent predictors of increased anxiety and depression symptomology included being female or transgender, being younger, having lower socioeconomic status, having pre-existing mental health conditions or chronic illness, having increased workload during the pandemic, being dissatisfied with teaching resources, and having negative changes in relationships with students, parents, or colleagues. The predictors collectively explained 16.4% of the variance in depression and 17.5% of the variance in anxiety symptoms. Conclusions: The significant mental health needs of teachers in Latin America and Spain during the COVID-19 pandemic highlight the pressing necessity of prioritizing the mental health needs of educators to improve their psychological health and children’s educational environments. These results also suggest the practical need to provide teachers with additional school-based supports and resources for future transitions to online learning or disruptions to traditional instruction.

Bryan R Christ, Jonathan Kropko, Thomas Hartvigsen. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

Bryan R Christ, Zachary Gottesman, Jonathan Kropko, Thomas Hartvigsen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Purpose: This study explored the potential long-term effects of academic-related variables, including academic satisfaction, college degree attainment, unmet academic accommodation needs, and demographic characteristics on the job and career satisfaction of adults with disabilities using modern machine learning techniques. Method: Participants (n = 409) completed an online survey assessing these constructs. All had a disability or chronic health condition, had attended school in the U.S. throughout their K-12 education, and were between 19 and 86 years of age. Results: The random forest models had 68.6% accuracy in correctly identifying job satisfaction and 72.5% accuracy in correctly identifying career satisfaction. When using mean decrease in impurity (MDI) and permutation importance to identify statistical predictors, academic satisfaction was the most important predictor of job satisfaction in both MDI and permutation importance, while unmet academic accommodations was the fourth highest predictor for MDI behind academic satisfaction, disability level, and age, but ahead of other demographic variables and college degree status, and the second highest predictor of job satisfaction in permutation importance. For career satisfaction, academic satisfaction accounted for the highest MDI, while unmet academic accommodations ranked fourth. For permutation importance, academic satisfaction ranked first, and unmet academic accommodations ranked fifth behind academic satisfaction, age, college degree status, and disability level. Discussion: Meeting the academic accommodation needs of disabled students is linked with lasting vocational success. This study underscores the associations between unmet academic accommodation needs and future job and career satisfaction, illuminated using novel machine learning techniques. To our knowledge, this is the first investigation of the potential long-term associations between unfulfilled accommodation needs and future job and career satisfaction.

The COVID-19 pandemic severely affected teaching and student outcomes throughout the world, yet the long-term impacts of the pandemic on teachers and students in Latin America and Spain remain largely underexamined. This study examined the experiences of 5416 teachers from 20 Spanish-speaking countries during and after the pandemic, with an emphasis on teachers’ health, teaching experiences, and school-based support; perceptions of their students’ experiences and struggles; and relationships with students, families, and colleagues. The study used an online survey design with recruitment primarily based on snowball sampling, with most variables being collected using Likert-style responses. Descriptive statistics as well as bivariate correlations between variables were calculated. The results suggested that teachers were mostly healthy and reported moderate fear of COVID-19, though there was much variability. While, on average, teachers did not believe the pandemic had had a negative impact on their relationships with parents, colleagues, and students, they believed that the pandemic had a meaningful negative impact on their students by increasing problems in five key domains: educational, behavioral, social, emotional, and familial. Teachers had received very little training regarding online teaching, although they were moderately satisfied with the resources they had received to teach online and the safety measures their schools had implemented. Schools should provide additional support to teachers, students, and families during times of prolonged stress such as pandemics to help mitigate their lasting effects on students’ academic and psychosocial functioning.