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Background Differences in the way autistic children experience the world can contribute to anxiety and stress. Carol Gray’s Social Stories™ are a highly personalised intervention to support children by providing social information about specific situations in an individual story. Objectives This randomised controlled trial aimed to establish whether Social Stories are clinically effective and cost-effective in improving social responsiveness and social and emotional health in children on the autism spectrum in schools. Design A multisite pragmatic cluster randomised controlled trial comparing Social Stories with care as usual. Setting Eighty-seven schools (clusters) across Yorkshire and the Humber. Participants Two hundred and forty-nine children were randomised via a bespoke system hosted at York Trials Unit (129 Social Stories and 120 care as usual). Recruitment was completed in May 2021. Participants were children aged 4–11 years with a diagnosis of autism, alongside teachers, interventionists and caregivers. Recruitment was via schools, NHS trusts, support groups and local publicity. Intervention The intervention included training for educational professionals and caregivers covering psychoeducation and implementation of Social Stories. Stories were written around contextualised goals around the child’s need for social information. Interventionists read the Social Story™ with the child at least six times over 4 weeks during school. Main outcome measure The primary outcome was the Social Responsiveness Scale-2 completed by teachers at 6 months (the primary end point), which measures social awareness, cognition, communication and behaviour. Data were collected from caregivers and educational professionals at 6 weeks and 6 months through questionnaires. Blinding of participants was not possible. Results At 6 months, the estimated difference in expected teacher-reported Social Responsiveness Scale-2 T-score (the primary end point) was −1.61 (95% confidence interval −4.18 to 0.96, p = 0.220), slightly favouring the intervention group. The estimated differences for the parent-reported secondary outcomes at 6 months were small and generally favoured the control group except the measure of children’s quality-adjusted life-year (+ 0.001, 95% confidence interval −0.032 to 0.035) and parental stress (−1.49, 95% confidence interval −5.43 to 2.46, p = 0.460), which favoured the intervention group. Children in the intervention group met their individual goals more frequently than children who received usual care alone (0.97 confidence interval 0.21 to 1.73, p = 0.012). The intervention is likely to save small costs (−£191 per child, 95% confidence interval −767.7 to 337.7) and maintain a similar quality of life compared to usual care. The probability of Social Stories being a preferred option is 75% if the society is willing to pay £20,000 per quality-adjusted life-year gained. Limitations include considerable disruptions during the coronavirus disease 2019 pandemic. Conclusion Social Stories are used in schools and represent a low-cost intervention. There is no clinically evident impact on social responsiveness, anxiety and/or depression, parental stress or general health. Benefits were observed for specific behavioural goals as assessed by the teacher, and Social Stories may serve as a useful tool for facilitating dialogue between children and school staff to address specific behavioural challenges. Usage should be at the school’s discretion. Future work Given the uncertainty of the results in light of coronavirus disease 2019, further work to establish the impact of Social Stories is merited. Trial registration This trial is registered as ISRCTN11634810. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 16/111/91) and is published in full in Health Technology Assessment; Vol. 28, No. 39. See the NIHR Funding and Awards website for further award information. Plain language summary Autism affects the way children experience the world, and some children find social situations stressful. We wanted to know whether Social Stories™, developed by Carol Gray, helped children with their social skills and behaviour in school and whether they offered value for money. A randomised controlled trial design was used, which gave schools an equal chance of being asked to deliver Social Stories or to continue providing care as usual. Two hundred and forty-nine children from 87 schools took part and we trained school staff and parents to write and deliver Social Stories. We agreed with teachers and parents, what each child needed help with and wrote stories with this in mind. Trained staff read the Social Story with the child at least six times over 4 weeks. Follow-up information was collected from parents and school staff at the start of the study, after 6 weeks and 6 months. After 6 months, teachers completed a questionnaire called the Social Responsiveness Scale-2 which measures the child’s social skills. Using these measures, the results suggest that Social Stories do not lead to any significant changes in social skills, mental health, parent stress, general health or quality of life but children in schools allocated to Social Stories met their goal more frequently and incurred less costs than children who did not. Parents and educational professionals found the Social Stories intervention and training beneficial. Based on our findings, Social Stories do not appear to improve general social skills in primary-aged autistic children. Benefits were observed for specific goals, and school-based costs were reduced. Scientific summary Sections of this summary have been adapted from the trial protocol by Wright et al. (Wright B, Teige C, Watson J, Hodkinson R, Marshall D, Varley D, et al. Autism Spectrum Social Stories In Schools Trial 2 (ASSSIST2): study protocol for a randomised controlled trial analysing clinical and cost effectiveness of Social Stories™ in primary schools. BMC Psychol 2020;8:60. https://doi.org/10.1186/s40359-020-00427-z). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article unless otherwise stated. Background Children on the autism spectrum experience a higher prevalence of mental health problems than typically developing children, including anxiety and low mood. Many children on the autism spectrum struggle to manage social anxiety and feelings of frustration, which can lead to behaviours that challenge them. The International Society for Autism Research (INSAR) has highlighted that more research evaluating early interventions for children on the autism spectrum is needed to ensure practitioners and policy-makers have robust data on intervention effectiveness and implementation to secure optimal outcomes for children. One intervention that attempts to alleviate social difficulties while not being intrusive, time-consuming or requiring extensive involvement from experts is Carol Gray’s Social Stories™. Social Stories are a highly personalised intervention aiming to share accurate, meaningful information about a particular goal or topic that the child needs help with in a positive and reassuring way. Social Stories can be written and delivered by both parents and professionals in a range of settings and represent a less time-consuming and intrusive intervention than alternatives. Previous studies examining the use of Social Stories have yielded mostly positive results but have largely been single-case studies with a lack of evidence from randomised controlled trials (RCTs). Despite the lack of rigorous evidence, numerous schools and families are already accessing Social Stories training and delivering the intervention with children and therefore a fully powered trial is timely. Schools have limited resources and limited access to specialist practitioner interventions, and therefore it is important that interventions such as Social Stories undergo robust evaluation. If they are found to be clinically effective and cost-effective, they can be delivered within schools on a day-to-day basis. Objectives The aim was to assess whether Social Stories alongside care as usual is clinically effective and cost-effective in improving child social impairment, reducing anxiety and improving social and emotional health in children on the autism spectrum in primary and special educational needs (SEN) schools when compared with care as usual alone. Primary objective The primary objective of the study was to establish whether Social Stories can improve social responsiveness in children on the autism spectrum in primary schools across Yorkshire and the Humber, when compared to children who have received care as usual only. Social responsiveness can be broadly defined as social awareness, social cognition, social communication, social motivation and mannerisms. Secondary objectives The secondary objectives of this trial were: to investigate whether Social Stories can reduce behaviours that challenge children on the autism spectrum in primary schools to investigate whether Social Stories can improve social and emotional health in children on the autism spectrum in primary schools to assess the cost effectiveness of Social Stories to examine the effects of Social Stories delivered in the classroom on general measures of health-related quality of life to examine whether Social Stories improve classroom attendance to assess sustainability of Social Stories in an educational setting across a 6-month period to examine any changes in parental stress to examine any associations between treatment preference and outcomes to examine how elements of session delivery (e.g. session frequency, length and any associated problems/adverse events) are associated with outcomes. Methods This trial was a multisite pragmatic cluster RCT comparing Social Stories and care as usual with a control group receiving care as usual alone. Care as usual is defined as the existing support routinely provided for a child with autism spectrum condition (ASC) from educational and health services such as specialist autism teacher teams, mental health teams or other associated professionals. The trial included an internal pilot, economic evaluation and a nested process evaluation. Setting The Social Stories intervention was primarily delivered within educational settings by educational professionals. Educational settings included both mainstream primary schools and SEN schools. Parents/caregivers were invited to receive Social Stories training and had the option of also delivering the intervention within the home. Participants Inclusion criteria The child was aged 4–11 years at the time of recruitment. The child attended a participating primary or SEN school within Yorkshire and the Humber. The child has a clinical diagnosis of ASC and daily challenging behaviour. Parents/guardians of the child were able to self-complete the English language outcome measures (with assistance if necessary). Exclusion criteria The school had used Social Stories for any pupil in the current or preceding school term. The child or interventionist teacher had taken part in the previous Social Stories feasibility study (ASSSIST-1). Schools that have taken part were not excluded. Where families were recruited but the child’s school was unwilling or unable to participate, families were notified by a telephone call and/or an e-mail. Where children were confirmed eligible and consent had been taken from all relevant parties, baseline questionnaires were distributed to parents/carers and educational professionals. A school was considered ‘ready to randomise’ once all consent forms and baseline questionnaires had been received by the trial team. Interventions Children in the intervention arm of the trial received the Social Stories intervention in addition to their care as usual. The Social Stories intervention was delivered by a trained educational professional (the interventionist) who was employed within each school allocated to the intervention arm. The interventionist varied between the schools [e.g. a teacher, teaching assistant (TA) or Special Educational Needs Coordinator (SENCO)] but was most typically a TA. A core aspect of the intervention was to first agree a goal around which the story would be set. The goal was typically a behavioural challenge the child was struggling with, for example, sharing with their teacher how they were feeling. This goal was agreed during a collaborative ‘goal-setting meeting’ attended by the child’s teacher, a parent/caregiver, a member of the research team and sometimes the child’s TA. Occasionally, the children themselves were able to feed into the goal-setting process if deemed appropriate. The intervention is designed to provide social information to the child, and so the goal sought to reflect this. In this way, many goals sought to reduce the child’s anxiety or frustration by equipping them with information or providing reassurance around appropriate behaviours within a given circumstance. We assessed goal attainment in terms of how frequently a child was able to implement the desired behaviours, for example, if a goal for a particular child was to use calming strategies when they were upset, we asked teachers to rate how often a child was able to do this. Interventionists were trained by members of the research team, who had received training via a cascade model overseen by child psychiatrist, Professor Barry Wright. The training of interventionists included some psychoeducation around the differences in children on the autism spectrum to facilitate understanding around what factors may be driving the observed behaviours associated with the behavioural goal. Training also provided key information on the design and implementation of Social Stories, with materials based on those developed in the preceding feasibility study with the support of Carol Gray and a Social Stories manual produced by Professor Barry Wright and a Clinical Psychologist with expertise in autism. During the training session, interventionists constructed a Social Story. Parents/guardians were also invited to attend these sessions. Following training, all Social Stories were assessed against a fidelity checklist by a member of the research team to ensure they conformed to the 10 established criteria central to Carol Gray’s Social Stories. They were then delivered to children in the intervention arm by the interventionist at least six times over a 4-week period. Sample size The primary outcome was that the teacher completed the Social Responsiveness Scale, Second Edition (SRS-2) T-score at 6 months. Within the pilot data, outcomes were measured at 6 and 16 weeks. The correlation between baseline and 6 weeks [r = 0.67, 95% confidence interval (CI) 0.44 to 0.80] was lower than that at 16 weeks (r = 0.83, 95% CI 0.68 to 0.91) for the pilot data. To be conservative, the lower 95% confidence limit was chosen for the lowest correlation between baseline and follow-up that we observed within our pilot data (r = 0.44). Assuming a difference of 3 points, standard deviation (SD) = 7, 5% alpha, 90% power, average cluster size 1.35, intracluster correlation coefficient (ICC) = 0.34, correlation = 0.44 and 25% attrition, a total sample size of 278 was required. Support was gained from the trial steering committee and funder to stop recruitment with 249 children, which we modelled would still retain at least 80% power under a variety of different scenarios. Randomisation Randomisation was completed by unblinded members of the trial team via a bespoke trial management system. Stratified blocked randomisation was used to allocate school clusters, with randomly varying block sizes (4, 6 and 8), stratification by school type (SEN school or mainstream school) and the number of participating children within a school (≤ 5 or > 5 participating children). There were no stipulations regarding the minimum number of participants per school cluster (i.e. any school with ≥ 1 eligible/consented child with available baseline data was eligible for randomisation). Main outcome measure The primary outcome of the trial was the SRS-2 at 6 months post randomisation, as reported by the child’s teacher. This was also collected at 6 weeks post randomisation. The SRS-2 identifies the presence and severity of social difficulty within the autism spectrum condition and consists of 65 questions. For each question, the person completing the form picks a score from 1 to 4 (1 = not true, 2 = sometimes true, 3 = often true, 4 = almost always true) that best describes the child’s behaviour. A T-score is calculated based on the sex of the child and the person completing the form (teacher). Economic evaluation The primary analysis for the economic evaluation was a within-trial cost-utility analysis conducted from a societal perspective. Combining costs and quality-adjusted life-years (QALYs), an incremental cost-effectiveness ratio (ICER) of cost per QALY was calculated and evaluated against the willingness-to-pay threshold of £20,000–30,000 per QALY gained to assess the cost effectiveness of Social Stories compared to usual care. Regression models on an intention-to-treat basis were used to compare mean costs and QALYs. To take uncertainty into consideration, a non-parametric bootstrap resampling method was used to produce CIs around the cost and QALY differences and ICER. The following sensitivity analyses were conducted to test assumptions made in the primary analysis: a complete case analysis; a sensitivity analysis from the UK NHS and personal social services (PSS) perspective; and a sensitivity analysis from the joint perspectives of the NHS/PSS and education perspectives to representative a global public sector perspective. Process evaluation The process evaluation was cross-sectional and longitudinal, encompassing all aspects of the Social Stories intervention. The aim of the process evaluation was to assess the fidelity of the programme, consider the views of various stakeholders and identify barriers and facilitators to successful implementation. We aimed to achieve this through a combination of data collection techniques, including interviews, focus groups, questionnaires, surveys and diaries (session logs). Statistical analyses For the primary outcome, differences in expected SRS-2 scores were estimated using a linear mixed-effect covariance pattern model with both post-randomisation time points (6 weeks and 6 months) included as outcomes and fixed effects for treatment group, time point and their interaction. Further fixed effects were included for the following cluster and participant-level baseline covariates: school SEN status (binary, SEN/non-SEN), number of consented children attending school (binary, ≤ 5/> 5), baseline score (linear term), age at randomisation (linear term) and sex (binary, female/male). Dependence between participants within a cluster was modelled using school cluster-level random intercepts, and dependence between repeated measurements within participants was modelled using an unstructured correlation matrix for the residual errors. Similar models were used to analyse the secondary outcomes. Results The primary analysis included all 249 randomised children. After 6 months, a reduction of 1.61 points was found on the SRS-2 in children on the autism spectrum in the intervention group (95% CI −4.18 to 0.96; p = 0.220). No statistically significant differences were found in overall symptoms of anxiety and/or depression, parental stress or general health. Children in the intervention group met their individual goals more frequently than children who received usual care alone, and this difference was statistically significant (0.97, CI 0.21 to 1.73; p = 0.012). The primary analysis was conducted under the principles of intention to treat. Coronavirus disease 2019 (COVID-19) impacted upon delivery in schools and school routines, and only 62.8% of the sample is known to have received the intervention as per protocol. Sensitivity analyses suggested that there may be a dose effect with improved clinical effects in those receiving the intervention as per protocol compared to those with fewer sessions. The economic evaluation showed that, compared to usual care, Social Stories slightly decreased the service use costs over the 6-month period by £191 (95% CI −337.7 to 767.7) per child and maintained similar QALYs. If society is willing to pay £20,000 for extra QALY gained, then the probability of Social Stories being a dominant and preferred option is 75%. The results of both primary and sensitivity analyses, which considered costs derived from various perspectives, are consistent but limited to the given data within the short study time frame, number of missing data and disruption of COVID-19. The qualitative results suggest parents and educational professionals found the Social Stories training and intervention beneficial. Limitations include considerable disruptions during the COVID-19 pandemic. Conclusion We found no impact of Social Stories on autistic children’s overall social responsiveness skills. There was some evidence that Social Stories are well supported by parents and teachers, and it may be effective at addressing a wider set of individual goals and could reduce costs. Based on the evidence generated through this trial, we cannot recommend Social Stories for the purposes of improving social skills, anxiety and/or depression, parental stress, general health and quality of life in autistic children. The COVID-19 pandemic had a large impact on schools during this trial, and sensitivity analyses suggested that poor compliance may have reduced the effectiveness of the intervention and that effects were better in those receiving Social Stories as per protocol. We did not find any negative effects, and Social Stories are already frequently used in schools to support autistic children and represent a low-cost and potentially cost-saving intervention. Despite limited evident impact on global social skills, based on the data elicited through the process evaluation, it appears that Social Stories may serve as a useful tool for facilitating dialogue between children and school staff to better understand the needs of autistic children, and usage should be at the school’s discretion. Trial registration This trial was registered as ISRCTN11634810. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 16/111/91) and is published in full in Health Technology Assessment; Vol. 28, No. 39. See the NIHR Funding and Awards website for further award information.

BackgroundMetadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. ObjectiveTo address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. MethodsThe framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. ResultsThe implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. ConclusionsThe adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

The following publication contains book reviews of these titles: • Olivier, J., Oojorah A., Udhin, W. (eds) (2022) Perspectives on teacher education in the digital age. Singapore: Springer Nature. 241 pages. ISBN 978-981-19-4225-9 • Isaacs, S., Mishra, S. (2022). Smart education strategies for teaching and learning: Critical analytical framework and case studies. Moscow: UNESCO Institute for Information Technologies in Education. 158 pages. ISBN: 978-5-906399-13-7. Open Access: http://hdl.handle.net/11599/4464 • Popenici, S. (2022). Artificial intelligence and learning futures: Critical narratives of technology and imagination in higher education. Routledge. 228 pages. ISBN: 978-1-032-20852-7 • Moore, S. L., Tillberg-Webb, H. K. (2023). Ethics and Educational Technology: Reflection, Interrogation, and Design as a Framework for Practice. Routledge. 328 pages. ISBN: 978-0-415-89508-8 • Conrad, D. (ed) (2023) Research, writing, and creative process in open and distance education: Tales from the field. Cambridge, UK: Open Book Publishers. 239 pages, ISBN Paperback: 978-1-80511-094-1

Background The completion of case-based surveillance forms was vital for case identification during COVID-19 surveillance in Malawi. Despite significant efforts, the resulting national data suffered from gaps and inconsistencies which affected its optimal usability. The objectives of this study were to investigate the processes of collecting and reporting COVID-19 data, to explore health workers’ perceptions and understanding of the collection tools and processes, and to identify factors contributing to data quality. Methods A total of 75 healthcare professionals directly involved in COVID-19 data collection from the Malawi Ministry of Health in Lilongwe and Blantyre participated in Focus Group Discussions and In-Depth Interviews. We collected participants’ views on the effectiveness of surveillance forms in collecting the intended data, as well as on the data collection processes and training needs. We used MAXQDA for thematic and document analysis. Results Form design significantly influenced data quality and, together with challenges in applying case definitions, formed 44% of all issues raised. Concerns regarding processes used in data collection and training gaps comprised 49% of all the issues raised. Language issues (2%) and privacy, ethical, and cultural considerations (4%), although mentioned less frequently, offered compelling evidence for further review. Conclusions Our study highlights the integral connection between data quality and the design and utilization of data collection forms. While the forms were deemed to contain the most relevant fields, deficiencies in format, order of fields, and the absence of an addendum with guidelines, resulted in large gaps and errors. Form design needs to be reviewed so that it appropriately fits into the overall processes and systems that capture surveillance data. This study is the first of its kind in Malawi, offering an in-depth view of the perceptions and experiences of health professionals involved in disease surveillance on the tools and processes they use.

Abstract Background Medication reviews in primary care provide an opportunity to review and discuss the safety and appropriateness of a person’s medicines. However, there is limited evidence about access to and the impact of routine medication reviews for older adults in the general population, particularly in the UK. We aimed to quantify the proportion of people aged 65 years and over with a medication review recorded in 2019 and describe changes in the numbers and types of medicines prescribed following a review. Methods We used anonymised primary care electronic health records from the UK’s Clinical Practice Research Datalink (CPRD GOLD) to define a population of people aged 65 years or over in 2019. We counted people with a medication review record in 2019 and used Cox regression to estimate associations between demographic characteristics, diagnoses, and prescribed medicines and having a medication review. We used linear regression to compare the number of medicines prescribed as repeat prescriptions in the three months before and after a medication review. Specifically, we compared the ‘prescription count’ - the maximum number of different medicines with overlapping prescriptions people had in each period. Results Of 591,726 people prescribed one or more medicines at baseline, 305,526 (51.6%) had a recorded medication review in 2019. Living in a care home (hazard ratio 1.51, 95% confidence interval 1.40-1.62), medication review in the previous year (1.83, 1.69-1.98), and baseline prescription count (e.g. 5-9 vs 1 medicine 1.41, 1.37-1.46) were strongly associated with having a medication review in 2019. Overall, the prescription count tended to increase after a review (mean change 0.13 medicines, 95% CI 0.12-0.14). Conclusions Although medication reviews were commonly recorded for people aged 65 years or over, there was little change overall in the numbers and types of medicines prescribed following a review. This study did not examine whether the prescriptions were appropriate or other metrics, such as dose or medicine changes within the same class. However, by examining the impact of medication reviews before the introduction of structured medication review requirements in England in 2020, it provides a useful benchmark which these new reviews can be compared with.

IntroductionPopulation health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources.MethodsThe INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis.ResultsThe INSPIRE datahub's “On-ramp” services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability.ConclusionThe pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.

Current trends in keyboard design show that QWERTY-similarity is a key factor for high user-adaptability keyboard design. This design approach has the challenge that the higher the similarity the lower the text-entry rate for the optimized keyboard. This article reports on the findings of an empirical study which we conducted on QWERTY-users to measure the effect of using an entry-level keyboard in improving user-ability to adapt to a new keyboard. The study used two Central-Bantu physical keyboards (entry-level: with high QWERTY-similarity, and advanced-level: with low similarity) which we had designed in an earlier study. The empirical study obtained learning-curves of the Advanced-level keyboard, of a 12-participant group which was first introduced to an entry-level keyboard, against a control-group of similar size, in a longitudinal study design. A two-sample t-test on the empirical results showed that the entry-level approach caused a marginally significant text-entry-rate improvement of 9.4% with p < .09. A two-sample U-test on word-error rates indicated a non-significant improvement of 8.4%. Our study has shown that the use of entry keyboards is an effective strategy in improving keyboard user-adaptability.

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.

Previous studies have shown that autistic people often display atypical responses when processing sensory information, with particular prevalence within the auditory domain. Often provoked by common everyday sounds, auditory hypersensitivity can result in self-regulatory fear responses. This can be potentially harmful to autistic individuals and the people around them and is associated with greater occurrence of anxiety, depression, and poorer overall quality of life in the autistic population. Rather than a physiological causation, the literature suggests that hypersensitivity to sound is likely to be caused by how auditory stimuli are processed in the brain. This paper reports a home-based digital intervention aimed to address auditory hypersensitivity in autistic children. Developed as an interactive virtual reality game, the system integrates exposure-based therapy techniques into game mechanics and delivers target auditory stimuli to the player rendered via binaural-based spatial audio. The performance of the platform was evaluated in a 10-week feasibility study, during which children (n = 7) engaged weekly with the game during a 30 min session. Following this period, a comparison of pre- and post-study measurements showed a decrease in sensitivity for five participants, with qualitative feedback highlighting an increase in tolerance towards real-world stimuli and challenging environments. These results provide initial support for SoundFields as a home-based intervention targeting auditory hypersensitivity experienced by autistic children.