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None of the many critical moments in Ireland’s often tumultuous history was more significant or defining than the Easter Rising of 1916. Central to the Rising was the Proclamation of Independence, in which Pádraig Pearse declared the new nation’s resolve to cherish all its children equally. CHERISHING ALL THE CHILDREN EQUALLY? brings together contributions from a range of disciplines to shed light on the processes of child development and to investigate how that development is influenced by a variety of demographic, family and socio-economic factors. Making extensive use of research and data that have emerged over recent years from the Growing Up in Ireland longitudinal study of children, the book considers whether or not all children can participate fully and equitably in contemporary Irish society. It asks whether or not we do, in fact, cherish all our children equally in modern Ireland, regardless of their family circumstances, health or ethnic background.

TABLES OF CONTENTS:

1. Introduction
2. Changing Perceptions and Experiences of Childhood, 1916-2016
3. Children and Families, Then & Now
4. Is Family Structure a Source of Inequality in Children’s Lives?
5. Parental Investment & Child Development
6. Inequalities in Access to Early Care and Education in Ireland
7. Inequalities from the Start? Children’s Integration into Primary School
8. Insights into the Prevalence of Special Educational Needs
9. The Experiences of Migrant Children in Ireland
10. Social Variation in Child Health & Development: A Life-course Approach
11. Child Access to GP Services in Ireland: Do User Fees Matter?
12. Anti-Social Behaviour at Age 13
13. Child Economic Vulnerability Dynamics in the Recession
14. Concluding Observations

Differentials in physical activity (PA) between social and economic groups has been shown to contribute significantly to social gradients in health and life expectancy, yet relatively little is known about why differentials in PA emerge. This paper uses longitudinal data on a nationally representative sample of 6,216 young people aged between 9 and 18, from Ireland, to measure the role of family, school and neighbourhood level factors in accounting for differentials in PA trajectories between groups of young people, defined by level of maternal education, whilst adjusting for the individual characteristics of the young person (sex, age, personality, body mass index and health-status). Levels of PA fall significantly across the sample between 9 and 18, and the decline in PA is larger for the children of lower educated mothers. We find a clear gradient in PA at each age by maternal education for both males and females. Descriptive analyses found social gradients in the majority of our risk factors. Using multi-level, linear spline regression models to decompose differentials between groups, we find that family-level mechanisms account for the biggest proportion of the differential in PA for both males (50.8%) and females (35.1%). Differences in income across maternal education categories accounted for 24.1% of the differential for males and 14.7% among females, making it the second most effective mechanism in explaining the social patterning of PA. Neighbourhood-level processes resulted in a modest reduction in the same differential, while school level processes had the effect of equalising differences in PA across maternal education groups.

Introduction
The COVID-19 pandemic wrought acute harm to mental wellbeing across the globe; not least through its impact on morbidity and mortality, but also from health anxieties, lockdowns and their economic fallout, the closure of key services, as well as the disruption of social networks. However, while the pandemic’s onset was global, not everyone experienced the same harm to their mental health. This study draws on information on the mothers of 12-year-olds from the Growing Up in Ireland (GUI) study to look at what helped cushion the negative impact of the pandemic on their mental health.

Data and Methods
The study draws on GUI data on Cohort ’08, a nationally representative sample of children born 2007-2008 along with their caregivers. In December 2020, the GUI team conducted a survey of primary caregivers (98 per cent of whom are mothers) during the COVID-19 pandemic. Mothers’ mental health is captured using the Short-form Center for Epidemiological Studies Depression Scale (CES-D), with higher scores indicating a greater risk of depression. The longitudinal nature of GUI means we can compare measures of mothers’ mental health during the COVID-19 pandemic (December 2020) with two previous timepoints before the pandemic – wave 3 (2013/2014) and wave 5 (2017/2018) – to see whether trends in depression changed significantly over the pandemic.

The study looks at four sets of factors: (1) mothers’ social resources, such as the level of social cohesion in their neighbourhood (e.g., trust and reciprocity among neighbours) or the support they receive from friends/family; (2) their economic resources, such as the share of income received from welfare, or how difficult they feel it is to ‘make ends meet’; (3) the quality of mothers’ local environment, such as the degree of traffic problems; and (4) their religiosity, such as how often they attend church. All of these factors are measured prior to the onset of the pandemic to reflect the resources mothers had at their disposal going into the crisis.

To understand how these buffering factors may have cushioned mothers’ mental health, the study uses a set of measures on what experiences mothers had during the pandemic. These include whether they found supervising their child’s schoolwork stressful, whether they experienced a loss of income/employment, or whether they had COVID or were anxious about friends/family contracting it.

Results

What factors cushioned the impact of the pandemic on mothers’ mental health?

In the years prior to the pandemic (2013/2014 to 2017/2018), levels of depression among mothers remained relatively low and stable. However, depression scores among mothers rose steeply with the onset of the pandemic (compared to 2017/18), nearly doubling. Yet, not all mothers experienced an equal increase in depression scores.

Stronger economic resources, and better household conditions, played a protective role: depression scores rose less among mothers who reported being able to ‘financially make ends meet’ easily (compared to those who found it difficult) and among mothers who lived in less overcrowded housing. The local environments in which mothers were living also helped cushion their mental health. Mothers who lived in neighbourhoods where heavy traffic was not a problem, and those who lived in rural areas, saw their depression scores increase less over the pandemic, compared to mothers in more urban areas or where traffic was a major problem.

The strongest protective factors were mothers’ social resources and their religiosity. Mothers who, just before the pandemic, had a partner in the household and who felt they got the help they needed from friends and family saw their depression scores increase less over the pandemic. Those who reported living in more socially cohesive neighbourhoods (where neighbours trust each other, exchange favours, or feel more attached) – what are termed in the research literature as high social capital areas – were more strongly protected against depression during the pandemic. Symptoms of depression also increased much more steeply among mothers who never attended church before the pandemic compared to mothers who used to attend daily or weekly (even though in-person services had not resumed at the time of the survey).

How did these buffering factors protect mothers’ mental health?

Social resources cushioned mothers’ mental health because, during the pandemic, these mothers experienced better family relations and struggled less with care work, home schooling, and their children’s return to school. Economic resources supported mental wellbeing by reducing financial stresses, enabling better home/outdoor environments, and likely allowing families to purchase the tablets, laptops, and high-speed internet needed to engage in home schooling and working more easily. A better-quality local environment allowed mothers to spend more time outside and improved family relations. It was harder to explain the protective role of religiosity, but it may relate to greater optimism or a stronger sense of meaning in life that help protect people from the stress of adversity.

Conclusion and policy implications
This study finds strong evidence that the social, economic, religious, and environmental characteristics in people’s lives protect their mental health when experiencing adverse life events. While some of these characteristics are personal, others can be influenced by policy. Investment in local infrastructure to enhance access to green spaces and reduce neighbourhood disorder will pay dividends in supporting the mental wellbeing of women and their families. Furthermore, community development initiatives to foster a sense of local belonging and trust will enhance families’ capacity to weather crises. Integrating such perspectives into crisis management could help protect societies, particularly more vulnerable groups, and potentially weaken the well-documented long-term scarring effects that adverse life events have on people’s lives.

Purpose: Using longitudinal data, this study aims to provide a greater understanding as to how parenting factors, including the employment of various disciplinary techniques, during a young person’s early adolescence may contribute to excessive Internet use (EIU) in later adolescence. Design/methodology/approach: Employing “Problem Behaviour” theory (PBT) as a guiding framework, this study uses data from the Growing Up in Ireland ’98 Cohort to investigate the effect of proximal and distal parental influences, measured when children were 13 years old, on symptoms of EIU in young adults at 17 or 18 years. Multiple regression models control for other child and family factors, and separate models for males and females examine sex differentials. Findings: Estimation did not find a statistically significant association between internet-specific mediation practices in early adolescence and EIU in later adolescence. However, regularly playing games or sports together is a protective factor. Parent-adolescent conflict and spending time home alone are estimated as risk factors. How parents deal with misbehaviour is a strong predictor of EIU, with the direction of association dependent upon the type and frequency of discipline employed. Practical implications: The findings are of practical significance in informing parents of modifiable aspects of their behaviour that can lead to EIU. Originality/value: The study applies a longitudinal modelling framework and considers the effect on EIU of various parental disciplinary techniques, representing a novel contribution.

Introduction: A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills. Objectives: The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys. Methods: The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children’s food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables. Results: The dashboard layout is structured by the YAML (YAML Ain’t Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny’s render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/. Examples of how to interact with the dashboard for selected oral health variables are illustrated. Conclusion: Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software.

The increasing importance of identifying lesbian, gay, bisexual and transgender (LGBT+) populations is a key driver in changes to demographic data collection in representative surveys of youth. While such population-based data are rare, Growing Up in Ireland (GUI), an Irish, government-funded, longitudinal survey, includes sexual orientation and gender identity (SOGI) measurements. This qualitative study responds to a query from the GUI study team and aims to identify how best to collect SOGI data in future waves of GUI. A university Human Research Ethics Committee granted approval for online consultations with LGBT+ youth (n = 6) with experiential expertise in policy making. The research is underpinned by rights-based public patient involvement (PPI) with recorded discussions, which were transcribed and imported into NVivo 12, generating the theme “recognition in research, policy and society”. This co-created article, with the LGBT+ young PPI Panel members, commends the inclusion of SOGI data in GUI and recommends changes in question placement and phrasing. Aligning with best practice, the PPI members provide a template for wording on consecutive sex and gender questions, expanded sexual orientation identity categories and maintaining the existing well-phrased transgender question from GUI. This offers potential to improve the quality of the SOGI data collected and the experience of those completing the questionnaire. These findings extend beyond GUI, with relevance for surveys with youth populations. This paper underscores the potential and benefits of participatory approaches to research with youth and views their role beyond simply as sources of data.

Keywords: LGBT+, sexual minority youth (SMY), gender minority youth (GMY), Growing Up in Ireland (GUI), SOGI measurement, quantitative, qualitative, survey design, PPI

The increasing emphasis on inclusive education internationally has broadened the definition of special educational needs (SEN) and greatly affected national prevalence estimates. In line with these international trends, in Ireland the EPSEN Act (2004) defines SEN as any “restriction in the capacity of a person to participate in or benefit from education”. Taking this broad definition, this study draws on the first longitudinal study of children in Ireland, Growing Up in Ireland, to generate a new estimate of SEN prevalence among Irish nine-year-old children. The analysis combines detailed information, collected from parents and teachers, encompassing diverse types of SEN, including physical disabilities, speech impairments, learning disabilities and emotional/behavioural difficulties. In doing so, the study establishes a SEN prevalence rate of 25 per cent among children in the mid-primary years, a rate very much in line with recent research in other European contexts. Additionally, the study details the diversity of data collected on children and young people with SEN and disabilities across agencies and government departments, the potential value of this data and directions for improved learner databases.