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Mounting evidence that growth mindset-the belief that intelligence is not fixed and can be developed-improves educational outcomes has spurred additional interest in how to measure and promote it in other contexts. Most of this research, however, focuses on high-income countries, where the most common protocols for measuring and intervening on student mindsets rely on connected devices-often unavailable in low- and middle-income countries' schools. This paper develops a toolkit to measure student mindsets in resource-constrained settings, specifically in the context of Brazilian secondary public schools. Concretely, we convert the computer-based survey instruments into text messages (SMS). Collecting mindset survey data from 3570 students in São Paulo State as schools gradually reopened in early 2021, we validate our methodology by matching key patterns in our data to previous findings in the literature. We also train a machine learning model on our data and show that it can (1) accurately classify students' SMS responses, (2) accurately classify student mindsets even based on text written in other media, and (3) rate the fidelity of different interventions to the published growth mindset curricula., (© 2024 Society for Research on Adolescence.)

Background: Cholera-like diarrheal disease (CLDD) outbreaks are complex and influenced by environmental factors, socioeconomic conditions, and population dynamics, leading to limitations in traditional surveillance methods. In Malawi, cholera is considered an endemic disease. Its epidemiological profile is characterized by seasonal patterns, often coinciding with the rainy season when contamination of water sources is more likely. However, the outbreak that began in March 2022 has extended to the dry season, with deaths reported in all 29 districts. It is considered the worst outbreak in the past 10 years., Objective: This study aims to evaluate the feasibility and outcomes of participatory surveillance (PS) using interactive voice response (IVR) technology for the early detection of CLDD outbreaks in Malawi., Methods: This longitudinal cohort study followed 740 households in rural settings in Malawi for 24 weeks. The survey tool was designed to have 10 symptom questions collected every week. The proxies' rationale was related to exanthematic, ictero-hemorragica for endemic diseases or events, diarrhea and respiratory/targeting acute diseases or events, and diarrhea and respiratory/targeting seasonal diseases or events. This work will focus only on the CLDD as a proxy for gastroenteritis and cholera. In this study, CLDD was defined as cases where reports indicated diarrhea combined with either fever or vomiting/nausea., Results: During the study period, our data comprised 16,280 observations, with an average weekly participation rate of 35%. Maganga TA had the highest average of completed calls, at 144.83 (SD 10.587), while Ndindi TA had an average of 123.66 (SD 13.176) completed calls. Our findings demonstrate that this method might be effective in identifying CLDD with a notable and consistent signal captured over time (R 2 =0.681404). Participation rates were slightly higher at the beginning of the study and decreased over time, thanks to the sensitization activities rolled out at the CBCCs level. In terms of the attack rates for CLDD, we observed similar rates between Maganga TA and Ndindi TA, at 16% and 15%, respectively., Conclusions: PS has proven to be valuable for the early detection of epidemics. IVR technology is a promising approach for disease surveillance in rural villages in Africa, where access to health care and traditional disease surveillance methods may be limited. This study highlights the feasibility and potential of IVR technology for the timely and comprehensive reporting of disease incidence, symptoms, and behaviors in resource-limited settings., (©Mariana Gasparoto Pereira Valerio, Beverly Laher, John Phuka, Guilherme Lichand, Daniela Paolotti, Onicio Leal Neto. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 16.07.2024.)

The transition to remote learning in the context of COVID-19 led to dramatic setbacks in education. Is the return to in-person classes sufficient to eliminate these losses eventually? We study this question using data from the universe of secondary students in São Paulo State, Brazil. We estimate the causal medium-run impacts of the length of exposure to remote learning during the pandemic through a triple-differences strategy, which contrasts changes in educational outcomes across municipalities and grades that resumed in-person classes earlier (already by Q4/2020) or only in 2021. We find that relative learning losses from longer exposure to remote learning did not fade out over time-attesting that school reopening was at the same time key but not enough to mitigate accumulated learning losses in face of persistence. Using observational and experimental variation in local responses across 645 municipalities, we further document that remedial educational policies in the aftermath of the pandemic boosted learning recovery., Competing Interests: Competing interests statement:G.L. is a cofounder at Movva, the implementing partner of the SMS intervention featured among the remedial policies evaluated in the paper.

Did the dramatic learning losses from remote learning in the context of COVID-19 stem at least partly from schools having overlooked students' socio-emotional skills-such as their ability to self-regulate emotions, their mental models, motivation, and grit-during the emergency transition to remote learning? We study this question using a cluster-randomized control trial with 18,256 high-school students across 87 schools in the State of Goiás, Brazil. The intervention sent behavioral nudges through text messages to students or their caregivers, targeting their socio-emotional skills during remote learning. Here we show that these messages significantly increased standardized test scores relative to the control group, preventing 7.5% of learning losses in math and 24% in Portuguese, consistent with the hypothesis that neglecting students' socio-emotional skills magnified learning losses during the pandemic., (© 2024. The Author(s).)

Background: In Côte d'Ivoire, cocoa farming is a widespread practice in rural households, an occupation with increased risks of depression and anxiety exacerbated by economic instability. We used the Goldberg-18 Depression and Anxiety diagnostic tool to identify predictors of depressive and anxiety symptomatology among a sample of parents in rural cocoa farming communities., Methods: In a cross-sectional survey, the Goldberg-18 was administered to Ivorian parents (N = 2471). Confirmatory factor analysis (CFA) was conducted to confirm the factor structure of the assessment tool, and Ordinary Least Squares (OLS) regression with clustered standard errors was used to identify sociodemographic predictors of symptomatology., Results: CFA showed adequate fit statistics for a two-factor model measuring depressive and anxiety symptoms. Among respondents, 87 % screened positive for requiring further referral for clinical diagnosis. Sociodemographic predictors of depressive and anxiety symptoms were similar for males and females. For the total sample, higher monthly income, more years of education, and belonging to the Mandinka ethnic group predicted fewer depressive and anxiety symptoms. In contrast, higher depressive and anxiety symptomatology were associated with age. Single marital status predicted increased anxiety but not depressive symptoms for the full sample model and the female only sample, but not the male sample., Limitations: This is a cross-sectional study., Conclusions: The Goldberg-18 measures distinct domains of depressive and anxiety symptoms in a rural Ivorian sample. Age and single marital status are predictors of increased symptoms. Higher monthly income, higher education, and certain ethnic affiliations are protective factors., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Whether SMS-based nudge interventions can increase parent engagement and improve child learning outcomes across diverse contexts such as rural West Africa is unknown. We conducted a school-randomized trial to test the impacts of an audio or text-message intervention (two messages per week for one school year) to parents and teachers of second and fourth grade students (N = 100 schools, 2246 students) in Cote d'Ivoire. Schools were randomly assigned to have messages sent to (i) parents only, (ii) teachers only, (iii) parents and teachers together, or (iv) control. There were statistically non-significant impacts of the parents-only treatment on learning, although with typical effect sizes (d = 0.08, p = 0.158), and marginally statistically significant increases in child labor (d = 0.11, p < 0.10). We find no impacts of the other treatment conditions. Subgroup analyses based on pre-registered subgroups show significantly larger improvements in learning for children with below-median baseline learning levels for the parents-only arm and negative impacts on learning for girls for the teachers-only arm, suggesting different conclusions regarding impacts on equity for vulnerable children., (© 2023. Springer Nature Limited.)

Background: The ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on health care providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via web-based surveys, has emerged in the past decade to complement traditional data collection approaches., Objective: This study compared novel PS data on COVID-19 infection rates across 9 Brazilian cities with official TS data to examine the opportunities and challenges of using PS data, and the potential advantages of combining the 2 approaches., Methods: The TS data for Brazil are publicly accessible on GitHub. The PS data were collected through the Brazil Sem Corona platform, a Colab platform. To gather information on an individual's health status, each participant was asked to fill out a daily questionnaire on symptoms and exposure in the Colab app., Results: We found that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we documented a significant trend correlation between lagged PS data and TS infection rates, suggesting that PS data could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast model based exclusively on TS data. Furthermore, we showed that PS data captured a population that significantly differed from a traditional observation., Conclusions: In the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive laboratory-confirmed tests. In contrast, PS data show a significant share of reports categorized as potential COVID-19 cases that are not laboratory confirmed. Quantifying the economic value of PS system implementation remains difficult. However, scarce public funds and persisting constraints to the TS system provide motivation for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic tradeoffs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, and shed light on its limitations and on the need for additional research to improve future implementations of PS platforms., (©Salome Wittwer, Daniela Paolotti, Guilherme Lichand, Onicio Leal Neto. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 26.04.2023.)

The role of executive function skills and motivation in supporting children's academic achievement is well-documented, but the vast majority of evidence is from high-income countries. Classrooms in sub-Saharan Africa tend to be large, teacher-driven, and lecture-focused, which may provide extra challenges for children to stay engaged in the learning process. Based in self-regulated learning theory, we tested the contributions of executive functions and motivation for children's literacy and numeracy skills over 1 school year. Our preregistered study of 2,500 primary school students in Côte d'Ivoire used lagged models with a robust set of demographic covariates. Executive functions were directly assessed, and children reported on their levels of motivation for schooling. Findings indicated strong longitudinal continuity of both literacy and numeracy skills over the academic year. Further, we found unique associations of executive functions and intrinsic motivation with changes in children's literacy skills and a unique association of executive functions, but not intrinsic motivation, with changes in children's numeracy skills over the school year. Overall, these results provide evidence for the importance of nonacademic skills for children's learning in a rural, sub-Saharan African context. Implications include improving access to preprimary education, the quality of primary school experiences, and teacher training and supports to increase children's early academic skills, self-regulation skills, and motivation for learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The transition to remote learning in the context of coronavirus disease 2019 (COVID-19) might have led to dramatic setbacks in education. Taking advantage of the fact that São Paulo State featured in-person classes for most of the first school quarter of 2020 but not thereafter, we estimate the effects of remote learning in secondary education using a differences-in-differences strategy that contrasts variation in students' outcomes across different school quarters, before and during the pandemic. We also estimate intention-to-treat effects of reopening schools in the pandemic through a triple-differences strategy, contrasting changes in educational outcomes across municipalities and grades that resumed in-person classes or not over the last school quarter in 2020. We find that, under remote learning, dropout risk increased by 365% while test scores decreased by 0.32 s.d., as if students had only learned 27.5% of the in-person equivalent. Partially resuming in-person classes increased test scores by 20% relative to the control group., (© 2022. The Author(s).)

Importance: School closures because of COVID-19 have left 1.6 billion students around the world without in-person classes for a prolonged period. To our knowledge, no study has documented whether reopening schools in low- and middle-income countries during the pandemic was associated with increased aggregate COVID-19 incidence and mortality with appropriate counterfactuals., Objective: To test whether reopening schools under appropriate protocols during the COVID-19 pandemic was associated with increased municipal-level COVID-19 cases and deaths in São Paulo State, Brazil., Design Setting and Participants: This observational study of municipalities in São Paulo State, Brazil, uses a difference-in-differences analysis to examine the association between municipal decisions to reopen schools during the COVID-19 pandemic and municipal-level COVID-19 case and death rates between October and December 2020. The study compared 129 municipalities that reopened schools in 2020 with 514 that did not and excluded data for 2 municipalities that reopened schools and closed then again., Main Outcomes and Measures: New COVID-19 cases and deaths per 10 000 inhabitants up to 12 weeks after school reopenings and municipal-level aggregate mobility for a subset of municipalities., Results: There were 8764 schools in the 129 municipalities that reopened schools compared with 9997 in the control group of 514 municipalities that did not reopen schools. The municipalities that reopened schools had a cumulative COVID-19 incidence of 20 cases per 1000 inhabitants and mortality of 0.5 deaths per 1000 inhabitants in September 2020 (the baseline period) compared with an incidence of 18 cases per 1000 inhabitants and mortality of 0.45 deaths per 1000 inhabitants during the baseline period in the comparison group. The findings indicated that there were no statistically significant differences between municipalities that authorized schools to reopen and those that did not for (1) weekly new cases (difference-in-differences, -0.03; 95% CI, -0.09 to 0.03) and (2) weekly new deaths (difference-in-differences, -0.003; 95% CI, -0.011 to 0.004) before and after October 2020. Reopening schools was not associated with higher disease activity, even in relatively vulnerable municipalities, nor aggregate mobility., Conclusions and Relevance: The findings from this study suggest that keeping schools open during the COVID-19 pandemic did not contribute to the aggregate disease activity., Competing Interests: Conflict of Interest Disclosures: Drs Lichand and Leal Neto reported personal fees from the Inter-American Development Bank (IADB) during the conduct of the study. Dr Cossi Fernandes reported being an employee of IADB. No other disclosures were reported., (Copyright 2022 Lichand G et al. JAMA Health Forum.)

Background: Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children's health and development outcomes more integrally from multiple perspectives., Objective: The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency., Methods: We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study., Results: During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks., Conclusions: This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development., (©Onicio Leal Neto, Simon Haenni, John Phuka, Laura Ozella, Daniela Paolotti, Ciro Cattuto, Daniel Robles, Guilherme Lichand. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 05.03.2021.)