The Classroom Learning Screening (CLS) as a Behavior Analytic Tool for Educational Equity: A Call to Action

Effecting policy change is a key strategy in tackling wider determinants of health. In England, public health sits within Local Authorities (LAs) and responsibility for ensuring health is considered across directorates increasingly falls to public health practitioners. While international professional standards expect competence in understanding policy processes, the advocacy role has been under-explored. This paper explores the professional skills, role characteristics and learning needs of practitioners advocating for the restriction of advertising high-fat, salt and sugar products in a region of England. A series of three interviews were conducted at three time points over 10 months with policy advocates leading this policy change from four LAs. Three focus groups were also held with 12 public health advocates from 10 LAs at the end of the 10-month period of data collection. Data were transcribed and analysed retroductively. Data showed that practitioners felt inexperienced as policy advocates and saw this work as different from other public health approaches. Successful advocates required interpersonal skills, knowledge of policy-making and local governance, determination, resilience, confidence, belief in their work's value and leadership. These skills were difficult to acquire through formal education, but advocacy training, mentorship and role modelling were seen as important for professional development. To successfully implement a Health in all Policies approach and address wider determinants of health, public health practitioners need to be equipped and supported as policy advocates. The advocacy role and the complex skills required need to be more fully understood by the public health profession and prioritized within workforce development at both local and national levels.

Intimate partner violence (IPV) is a major public health problem in Africa and internationally, with consequences that include physical injury, significant morbidity and even death. The Rwandan 2005 Demographic and Health Survey (DHS) reported a national prevalence of IPV among pregnant women of 10.2% but there were limited data available on the factors involved. The aim of this study was to determine the factors associated with and prevalence of IPV among pregnant Rwandese women in the rural southern province of Kabutare. A total of 387 pregnant women attending antenatal clinics in the South Province of Rwanda answered a questionnaire which included items on demographics and IPV. Mean age and prevalence of IPV in the previous 12 months as well as lifetime IPV were assessed. Both univariate and multivariate odds of IPV exposure were estimated using logistic regression analysis. The mean age of the 387 participants was 29.4 years (SD 6.3 years). More than one in two participants reported lifetime verbal abuse (53.7%). Other forms of lifetime IPV included pulling hair (9.4%), slapping (18.2%), choking (6.5%), punching with fists (19.3%), throwing to the ground and kicking with feet (12.8%), and burning with a hot liquid (3.1%). In the multivariate analysis, alcohol use by male partner was positively associated with lifetime IPV (OR = 2.52; 95% CI [1.35, 4.71] for occasional drinkers, and OR = 3.85; 95% CI [1.81, 8.21] for heavy drinkers). Compared with subjects with no formal education, women who had elementary education were less likely to report lifetime IPV (OR = 0.30; 95% CI [0.11, 0.78]). Alcohol use by male partner and low education of women were positively associated with lifetime IPV. The high rates of IPV among Rwandan pregnant women indicate the need for urgent measures to prevent and curb domestic violence using public health education, an awareness campaign and policy advocacy.

Although progress has been made in examining education in northern Nigeria, literature has yet to focus on the reasons why male children are prevented from going to school in northern Nigeria. This study explores the reasons why Hausa and Fulani children are prevented from gaining formal education in northern Nigeria. The aim of this study was to explore: 1) factors that hinder attainment of formal education for children in northern Nigeria; and 2) the impacts of this discrimination on the children's families, northern Nigeria, and Nigeria in general. This group of men who are educationally discriminated against is known as the almajiri. Under the almajiri system, parents send their children, mostly boys aged 4–12, to distant locations to acquire Qur'anic education. This is a qualitative study, with data gotten through key informant interviews with 11 children and youths, and relevant academic literature was used to substantiate the data collected. It was analysed using Colaizzi's (1978) method of data analysis. The KII was conducted physically and over the phone. Emerged themes included: (1) fear of indoctrination; (2) economic benefits; (3) political benefits; (4) political benefits; (5) physical abuse; (6) sexual abuse; and (7) a high rate of illiteracy. Thus, it is concluded from the findings that children in northern Nigeria are deprived of formal education. Thus, policy advocacy and engagement with religious and traditional leaders by the government of northern states would help in addressing the problems. Policy implications and subsequent recommendations were discussed.

This article examines the critical role of unpaid care workers in early childhood development (ECD) across Africa, highlighting their indispensable contributions despite facing systemic challenges. These caregivers, predominantly women, provide essential nurturing and support during children’s formative years, yet they often lack access to resources, training, and formal recognition (ILO, 2023; APHRC, 2023). The gender disparities in unpaid care work are pronounced, with women shouldering a disproportionate burden that restricts their participation in formal education and employment (Stats SA, 2021). The study reviews existing interventions, focusing on the work of organisations like the Early Learning Resource Unit (ELRU). ELRU provides comprehensive support to caregivers through home visiting, early learning playgroups, and centre-based services, enhancing cognitive development and building caregiver capacity (ELRU, 2022). To strengthen these efforts, the article proposes several recommendations. Collaborative partnerships between ELRU and other stakeholders can amplify impact by addressing systemic issues such as unpaid stipends for ECD centres (LRC, 2024). Policy advocacy is crucial for reforming policies to benefit unpaid care workers, including their inclusion in the Unemployment Insurance Fund (UIF) and tax benefits for childcare expenses (Harambee, 2023). Expanding capacity-building initiatives is vital, involving comprehensive training modules tailored to informal caregivers’ needs (ELRU, 2022). Establishing more community-based ECD programmes can enhance access to quality childcare services and foster collective responsibility for care work (APHRC, 2023). Mobilising resources to support ELRU’s programmes is critical, ensuring consistent financial support for ECD centres (ETDP SETA, 2020). This research underscores the importance of addressing unpaid care workers’ needs as a strategic investment in Africa’s future. By reducing the burden of unpaid care work and enhancing support systems, Africa can unlock its children’s potential while advancing gender equality and fostering inclusive economic growth. Unpaid care workers are not merely caregivers but architects of community-driven educational progress and enablers of sustainable development, shaping future generations equipped to drive positive change across Africa.

Background: The 2018 Declaration of Astana identifies traditional knowledge (TK) as one of the drivers for strengthening primary health care systems through the use of technology (traditional medicines) and knowledge and capacity building (traditional practitioners). While TK underpins both traditional practice and the use of traditional medicines, facilitating the use of TK in contemporary health care systems has been difficult to achieve. The aim of this study was to identify key factors related to the translation of TK into contemporary settings to help establish tools to support the knowledge translation process. Methods: This study used World Café methodology to collect the observations, ideas, and perspectives of experts who use TK in their practice. These experts (n = 9) were from a variety of contexts, including clinical practice, research, education, policy, and consumer advocacy, participated in the 1-day event. Data were collected into NVivo 12 software and analyzed using inductive-deductive thematic analysis. Results: Thematic analysis identified five themes: the need to define the elements required for critical evaluation of sources of TK as evidence, the importance of applying a tradition-centric lens when translating TK for contemporary use, the need to bridge gaps between TK and its contemporary applications, the value of critically evaluating the TK translation process itself, and the recognition of traditions as living systems. Taken together, the themes showed holistic interpretation of the translation process that incorporates critical analysis of the TK itself and accountable, transparent, and ethical processes of translation that consider safety, socioeconomical and intellectual property impacts of TK in contemporary use. Conclusions: Stakeholders identified TK as a valid and important source of evidence that should guide practice in a range of contemporary settings (e.g., policy and clinical practice), and outlined important consideration for critiquing, evaluating, communicating, and using TK within these settings.

Strengthening non-formal religious education is an important aspect in efforts to empower village communities. Village governments have a central role in designing policy models that focus on strengthening religious education at the local level. In this research, a qualitative approach was used, taking data from various sources through library research. The research results found that village government policies in implementing non-formal education include several key aspects including community participation, partnerships with religious institutions, provision of adequate facilities and resources budgeted in the APBDes, as well as integration with formal education. Community participation in planning and implementing non-formal religious education programs directs programs that are more relevant to local needs and more supported by the community, because these policies are based on participatory principles and are a joint commitment between the community and the village government as stated in the RPJMDes and RKPDes policy documents. Establishing partnerships with religious institutions is a strong basis for supporting and integrating non-formal religious education in villages. Because formal religious institutions which are also managed by the government have human resources who are trained in the field of religion and can become teachers or facilitators in non-formal religious education activities, due to the weaknesses and limitations of human resources in the village. Next, providing adequate facilities and resources is a prerequisite for creating a conducive learning environment. The availability of classrooms, learning facilities, books and adequate operational funds will improve the quality of non-formal religious education in the village. Integration with formal education is also an important step in strengthening non-formal religious education. Collaboration between formal and non-formal education creates a learning environment that is holistic and relevant to students' needs. Through this policy model, strengthening non-formal religious education in villages can empower the community and increase their knowledge, skills and religious awareness. Continuous evaluation and monitoring are important tools in measuring program effectiveness and ensuring the continuity of community empowerment efforts through religious education at the village level. Keywords; Village Government Policy Model, Non-Formal Religious Education, Community Empowerment

Background Early childhood caries (ECC) remains a significant public health challenge, affecting young children and contributing to long-term oral health disparities. Traditional risk assessment tools often lack scalability and predictive accuracy. Machine learning (ML) models trained on national child health and social registries offer a promising solution for early, scalable ECC risk prediction in public health systems. Methods We constructed a retrospective nationwide cohort using the FinRegistry project, which links electronic health, demographic, and family data across Finland. Due to pending access to confirmed ECC diagnoses, a simulated ECC outcome with 20% prevalence was generated. A Random Forest model was trained on a 70/30 training/testing split to assess technical feasibility. Predictor importance was extracted to evaluate variable relevance. The study population included registry-linked individuals with complete demographic and household information. Results Preliminary models showed strong discrimination between simulated ECC cases and controls. Key predictive variables included receipt of social assistance, number of children in the household, maternal marital status, and indicators from the infectious disease registry. Model performance, measured by AUC, reached 1.00 on the simulated dataset. These results demonstrate that registry data structure can support early risk stratification for ECC using ML. Conclusions Our findings support the technical feasibility of using registry data and machine learning for ECC risk prediction. While current results are based on simulated outcomes, they provide a foundation for future application with real diagnostic data. This approach can inform early preventive targeting and strengthen precision public health strategies for oral disease prevention. Key messages • Machine learning models can be applied to linked national registry data to assess feasibility for predicting early childhood caries risk. • Registry-based prediction models can support early identification of high-risk children and inform future preventive oral health strategies.

Smart livestock farming systems may provide real-time on-farm scenarios enabling fast interventions that benefit the current herd or flock. Smart decision-making technologies refer to more precise control over livestock production processes, helping farmers improve their productivity and profitability. Livestock process parameters are often faced with inaccurate, incomplete, or even conflicting data, and a way of minimizing this effect when processing data is the use of non-classical logic. The use of conceptual non-classical logic might improve smart tools allowing for non-intrusive assessment of health status and welfare, where information can be collected without the stress of disturbing or handling animals. Continuous monitoring can also offer a more complete picture of the overall health and/or well-being of animals rather than a view in time, as provided by traditional assessment. Alerting farmers to problems as they arise in real-time allows for immediate and targeted interventions to benefit the current herds or flocks. This book chapter introduces the fundamentals of managerial processes using non-classic logic and data mining and offers several applications to improve the decision-making of smart livestock farming.KeywordsDecision-makingSmart livestock farming

ObjectiveSundown syndrome (SS), or sundowning, is a neuropsychiatric phenomenon marked by the worsening of symptoms in the late afternoon or evening, primarily in individuals with dementia. By systematically examining previous studies, this scoping review aims to (1) bridge traditional questionnaire-based assessment methods with advanced sensor-based tools and (2) propose a multimodal framework to guide future research in enhancing risk identification, diagnosis, monitoring, and treatment across key symptom categories.MethodWe conducted a comprehensive review of Web of Science, PubMed, Medline, APA PsycInfo, and IEEE Xplore to identify studies on SS. Following established scoping review guidelines, 13 review papers and 41 empirical studies were selected and analyzed based on traditional questionnaire-based observation and/or sensor-based measurement methods.ResultsWe identified key limitations in traditional assessment methods and classified SS symptoms into five domains: psychomotor symptoms, cognitive and perceptual disturbances, mood and affective symptoms, psychosis, and disruptions in activities of daily living and instrumental activities of daily living. Building on these insights, we proposed a multimodal platform integrating sensor technologies to enhance risk identification, diagnosis, continuous monitoring, and treatment.ConclusionsThis study advances the understanding of SS by synthesizing prior research, refining symptom domains, and proposing a roadmap for future investigation and intervention. The integration of multimodal sensor technologies holds the potential to reduce caregiver burden, enhance patient care, and enable more effective management of SS and other behavioral disturbances in older adults.

Patients undergoing major abdominal surgical procedures are at risk of postoperative complications, requiring early recognition. Clinical deterioration is preceded by changes in vital sings, which are measured three times a day by a nurse. Due to the intermittent measuring, this may result in a delay in the recognition of clinical deterioration. Continuous vital sign monitoring through wireless sensors offers a potential solution for earlier recognition. To evaluate user satisfaction of a new wireless monitoring system measuring vital signs continuously, by both patients and healthcare providers. A prospective, questionnaire-based study. From December 2021 to November 2022, user experience questionnaires were administered to patients who underwent major abdominal surgical procedures and received the patch postoperatively. Questionnaires were administered as well to nurses and physicians working on a surgical ward with the patch. Continuous measurements of heart rate, respiratory rate, and temperature were taken using the Sensium wireless patch. A total of 298 respondents completed the questionnaire, 191 patients, 88 nurses,and 19 physicians. Of the patients, 69% had a positive experience with the patch, and 74% felt safer. Sixty-three percent of the nurses were positive, and 65% had the feeling that they could monitor the patients better this way. Of the physicians, 63% were positive, 32% believed clinical deterioration could be identified earlier. The use of the Sensium wireless patch for continuous monitoring of vital signs postoperatively was found to be feasible and well-tolerated. Patients, nurses, and physicians reported a positive experience with its use.

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

A cumulative risk assessment is generally intended to address concurrent exposure by all exposure routes to a group of chemicals that share a common mechanism of toxicity. However, the contribution of different exposure routes will change over time. This is most critical when estimating risks to infants and children because their exposure sources change rapidly during the first few years of life because of dietary and behavioral changes. In addition, there may be changes in sensitivity to toxicants during this time period, associated with various developmental stages. Traditional risk assessments do not address this progression. Examples of how these factors might be incorporated into an early life risk assessment are provided for lead, dioxins and furans, and organophosphate pesticides. The same concepts may apply to other potentially susceptible subpopulations, such as the elderly.

IntroductionAutism spectrum disorder (ASD) is one of the national mental health priorities that has manifested a wide variability in practice in the Kingdom of Saudi Arabia (KSA). This work aimed to adapt evidence‐based clinical practice guidelines (CPGs) for ASD to synthesize the first national CPG for the management of children with ASD in KSA.MethodsThe CPG adaptation group comprised multidisciplinary expert clinicians and a CPG methodologist following the KSU‑Modified‑ADAPTE methodology. The last search date for source evidence‐based guidelines was March 2022.RecommendationsThree main categories of recommendations were included: (i) prevention and early identification, coding, psychometric tools, telehealth, risk factors and referral criteria, (ii) diagnosis, differential diagnosis, investigations and family support, (iii) interventions with problem minimization and avoidance, treatment goals, physical wellbeing, nonpharmacological interventions, sensory integration, parent‐mediated interventions, cognitive behavioural therapy, pharmacological interventions, psycho‐education of the family, special cases or comorbidities, sleep management, gastrointestinal and feeding interventions, the transition of care from paediatrics to adulthood. CPG implementation tools included a baseline assessment tool, clinical scenarios, pathways, quality measures, referral forms, screening tools and useful online resources. The adapted CPG presents practical, evidence‑based guidance with implementation tools for managing children with ASD in KSA. The project illustrated the applicability of the KSU‑modified‑ADAPTE and highlighted the importance of collaboration between clinicians and methodologists for adapting national CPGs.

The biggest challenge for the use of “big data” in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom. One of our findings from this research seems at first blush so obvious that it is hard to believe it has been overlooked in the design and implementation of health-care innovation technologies. Namely, people imagine data in very different ways. Understanding this key fact about data helps us understand why so-called “big data” solutions to health care are so difficult to implement in practice. Doctors, patients, and health-care entrepreneurs all value data in very different ways. One physician simply said, “I don't need more data; I need more resources.”* Saying this in Silicon Valley or at TedMed would be tantamount to heresy. Ditto for those of us who work in research and spend our careers collecting, massaging, managing, analyzing, and interpreting data. From the doctor's perspective, though, data require (and do not save) extra interpretive, clerical, and managerial labor. This perspective on “data,” at least with regard to current clinical practice, is that data use up more resources than the benefits they provide. In other words, most doctors think data innovation means more work for them, not less, and takes away time from what they see as their key priorities in providing quality care. In another setting, we observed nurse-practitioner case managers in a Medicare demonstration project working with a simple algorithm parsing patient-entered health data. Combined with case management, these data provided a look into the daily health of chronically ill elderly patients and a pathway for the care when it was needed. The data in that project were tightly tied to medical expertise within an existing clinic where a trusted person could initiate a chain of care responses. Although widely recognized as a clinical success, Medicare pulled the plug on the project for financial reasons—expertise is expensive. These two reactions to data-intensive pilot projects highlight the dilemmas of data-analytic approaches to health care. Businesses are in the thrall of the possibilities of ever-increasing predictive analysis on expanding troves of generated data. While the business and technology sectors see data as valuable, doctors often see data as costs, risks, and liabilities. And for many in health care, data are not seen as a source of value, but of additional work. Without the work needed to make data valuable and useful in particular settings in particular contexts in health care, big data will never solve problems. To turn a technology truism on its head, data in health care will never be free. And yet, the ways in which health technology innovators have talked about the power of data neglects key aspects of the social interoperability or integration of data into health solutions. How will such data be integrated into care providers' work practices; through the complex routines of clinics and hospitals; and into existing legal, social, political, and economic frameworks? These questions are enormous. Until we solve these questions of social interoperability, the risks presented by “big data” in health will outweigh the benefits to any particular individual, regardless of whether we're talking about terabyte-scale analytics or the “small-data” of n=1 individuals. What follows is an outline of how to tackle these questions based upon what our team has seen throughout our research.

Objective. To examine the link between ambient air pollution and poor pregnancy and neonatal outcomes. Methods. This systematic study searched numerous databases, including PubMed, Scopus, Web of Science, and Cochrane Library, revealed 26 papers that met established criteria. This research looked at how pollutants such as Particulate matter smaller than 2.5 microns, Particulate Matter ≤10 micrometers, Nitrogen Dioxide, Sulfur Dioxide, Ozone, and black carbon affected maternal and new-born health, including miscarriage, preeclampsia, preterm delivery, low birth weight, and neonatal respiratory and neurological abnormalities. Results. Findings repeatedly revealed that enhanced the danger of gestational problems & poor neonatal consequences, with pollutants including Particulate matter smaller than 2.5 microns and Nitrogen Dioxide substantially related to hypertensive disorders, before the expected time of delivery, low birth weight, and reduced new-born immune and respiratory function. The paper also discusses how pollution impacts health via biological processes such as oxidative stress and epigenetic alterations. Variability in research designs, exposure assessment methodologies, and regional pollution levels were observed. Conclusion. This review underscores the link between ambient air pollution, particularly Particulate matter smaller than 2.5 microns and Nitrogen Dioxide, and poor pregnancy and neonatal outcomes. Recognizing these risks is crucial for nursing care, allowing nurses to educate, identify early risks, and advocate for policies that protect mothers and newborns. Strengthening interventions will improve health outcomes for both.