The Nurturing Care Framework (NCF) calls for establishing a global monitoring and accountability systems for early childhood development (ECD). Major gaps to build low‐cost and large‐scale ECD monitoring systems at the local level remain. In this manuscript, we describe the process of selecting nurturing care indicators at the municipal level from existing routine information systems to develop the Brazilian Early Childhood Friendly Index (IMAPI). Three methodological steps developed through a participatory decision‐making process were followed. First, a literature review identified potential indicators to translate the NCF domains. Four technical panels composed of stakeholders from federal, state and municipal levels were consulted to identify data sources, their availability at the municipal level and the strengths and weakness of each potential indicator. Second, national and international ECD experts participated in two surveys to score, following a SMART approach, the expected performance of each nurturing care indicator. This information was used to develop analytical weights for each indicator. Third, informed by strengths and weaknesses pointed out in the previous steps, the IMAPI team reached consensus on 31 nurturing care indicators across the five NCF domains (Good health [n = 14], Adequate nutrition [4], Responsive caregiving [1], Opportunities for early learning [7] and Security and safety [4]). IMAPI represents the first attempt to select nurturing care indicators at the municipal level using data from existing routine information systems.

The employment of video surveillance cameras by public safety agencies enables incident detection in monitored cities by using object detection for scene description, enhancing the protection to the general public. Object detection has its drawbacks, such as false positives. Our work aims to enhance object detection and image classification by employing IoU (Intersection over Union) to minimize the false positives and identify weapon holders or fire in a frame, adding more information to the scene.

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone’s photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Background: Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Methods: 56 medication-naïve ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specific coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. Results: ARMS individuals showed a reduction in one gesture category.Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of amount of movement previously analyzed. Conclusion: The reduction in gesture performance observed agrees with literature findings in other cultural contexts in ARMS and schizophrenia subjects. The lack of differences for other categories might be related to differences within the ARMS group itself and the course of the disorder. These findings show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.

Spoken language is a key source of information for thought disorder evaluation. In the last decades, researchers linked psychopathology phenomena to their counterparts in natural language processing (NLP) analysis. Nonetheless, seemingly opposite traits remain unconciliated. For instance, psychotic speech comprises incoherent trails, but also highly associated ones. In order to address some of the remaining gaps, we leveraged procedures from dynamical systems and graph theory. We examined transcribed interviews of 133 individuals — 60 in at-risk mental states (ARMS) and 73 healthy controls — screened from 4,500 quota-sampled citizens in a large metropolis. SIPS was used to assess psychotic symptoms. NLP features were correlated with psychotic traits (Spearman’s ρ) and ARMS status (Wilcoxon signed-rank tests, general linear models and ensemble machine learning algorithms). The general trait (ω), negative, disorganized and general symptoms were correlated with snippets made of consecutive similar words. Namely, their frequency, average/maximum size, heterogeneity and the average number of unrelated words between such snippets. Positive symptoms were associated with adjective use. Average graph centrality was inversely correlated with the general trait. NLP features presented good performance as input in machine learning classification using the AdaBoost model with Random Forests as base learner (F1 score: 0.83, AUC: 0.93, Balanced Accuracy: 0.86).The existence of loosely connected words (e.g. incoherence, looseness, derailment) is well studied. Conversely, NLP models of perseveration (e.g. higher likelihood of chaining together islands of closely related words) and circumstantiality are brought forth in this work. Evidence shows good performance of NLP for clinical decision support in ARMS screening and assessment of subclinical psychosis. We show that a blueprint for speech-based psychometric evaluation is only a few pieces away. We highlight these fields for future research: clanging (a low hanging fruit), environmental context, task-related differences and interpersonal interactions.