Infant and Child Development最新文献

Recent studies have focused on predictors of exclusionary practices in early childhood, but few have examined what happens after a child is removed from care. Families' difficulty finding new care is complicated by the shortages of convenient, affordable and quality childcare in the United States. Using online surveys, we gathered data from parents whose children received multiple behavioural warnings from early education programmes (N = 218). Parents completed retrospective survey questions about their perceptions of each care arrangement's quality, accessibility and affordability. Half of the children were formally or informally expelled for their behaviour, while the other half were retained or left for reasons other than behaviour (e.g., graduating to kindergarten). Children were mostly male (65.6%), White (77.98%) and 42.66% had a diagnosed or suspected disability. This exploratory report describes the differential perceptions and experiences of families excluded from and retained by early education programmes following behavioural incidents. Results show that, for the most part, parents perceived subsequent programmes as relatively equivalent. However, nearly a quarter of excluded children did not attend a subsequent programme, and 10% were excluded more than two times before kindergarten entry. Findings are discussed in light of the broader research on early childhood exclusionary discipline.

Delay of gratification tasks have an impressive predictive value for various outcomes and are designed to measure self-regulation. Since many behavioural and psychological conditions in children are related to limitations in self-regulation, the extent to which delay tasks can be used as a screening for the detection of psychopathology is examined. Children from the general population (non-clinical sample; N = 1498; 51% girls) participated in delay tasks at the ages of 3 and 5. Parents rated ADHD and conduct problems when children were age 5 and 6, which we classified using cut-offs. Delay at age 3 was related to ADHD at age 5 (OR = 1.84) and conduct at age 6 (OR = 2.61). The results showed high specificity (77%–78%) and high negative predictive values (95%–98%), correctly identifying children below the SDQ cut-off, but low sensitivity (27%–42%), making the task unsuitable as a screening tool for children with an increased likelihood of developing psychopathology. These results were aggravated when only the first 20 s were considered, showing better specificity but worse sensitivity values.

Although the role of the home in supporting early childhood development, early learning and school outcomes is well established, the perspectives of caregivers on child development and schooling outcomes are comparatively underexplored. This qualitative study was conducted with caregivers of children aged 6–10 years in Mahikeng, South Africa and aimed to explore their beliefs related to the interconnected developmental continuum of ECD, school readiness and educational outcomes. A total of 18 caregivers participated in focus group discussions, including 2 males and 16 females. Qualitative data were analysed using reflexive thematic analysis. We developed four themes: being present; guiding children through school; influencing language and cognitive development and raising the next generation. Caregivers expressed beliefs that they played an important role in early language and cognitive development and in socioemotional development for older children. However, their behaviour was not necessarily motivated explicitly by child outcomes. Additionally, caregivers described many contextual factors, such as high levels of unemployment and crime, that may constrain the ability of households to support child development. Design of effective caregiving interventions must be informed by contextual understanding and help to overcome these barriers.

The mental health and well-being of children and adolescents are critical public health concerns globally. This cross-sectional study analysed nationally representative data from the combined 2016–2022 National Survey of Children's Health (n = 239,534) to produce estimates of parent-reported diagnoses of children's anxiety, depression and behavioural or conduct problems, and to examine temporal trends for each condition. Bivariate analyses and multivariable logistic regression were used to examine the prevalence of the conditions by sociodemographic characteristics. Among children aged 3–17 years, 8.4% had anxiety, 3.7% had depression and 7.1% had behavioural or conduct problems. Between 2016 and 2022, there were increases in the prevalence of anxiety (6.9%–10.6%), depression (3.1%–4.6%) and behavioural or conduct problems (7.2%–7.5%). Older age and higher poverty were associated with higher odds of each condition, while having private insurance was associated with lower odds. Females had higher odds of anxiety (AOR = 1.22; 95% CI, 1.15–1.29) and depression (AOR = 1.48; 95% CI, 1.35–1.61), and lower odds of behavioural or conduct problems (AOR = 0.45; 95% CI, 0.41–0.48). Prevention, early intervention and improved access to mental health services are critical to reversing the ongoing trends of increasing prevalence of mental health conditions among children and adolescents.

We introduce Speech Transcriber with Rev AI (STR) - a Python script that allows for easy interfacing with the Rev AI speech transcription service. Recent advancements in technology have led to increased accuracy and affordability of automatic transcription services, making them preferable over the laborious and time-consuming process of manual transcription. STR allows users to take advantage of speech-to-text transcription services to transcribe their own verbal response data. STR is partially tailored to child development researchers utilising the Codes for the Human Analysis of Transcripts (CHAT) though the code is generic enough to output unformatted transcriptions. STR allows transcription of single words and multi-speaker dialogues in 50+ languages. We describe STR, provide a tutorial for CHAT-formatted transcriptions, describe settings available for customising transcription and conduct a brief analysis of the efficiency and accuracy of transcription. Speech that was transcribed in over half an hour by trained transcribers was transcribed in less than two minutes (with ~90% accuracy) by Rev AI. Considering the additional time needed for error correction and CHAT formatting, we estimate that manual transcription takes twice as long as transcribing with assistance from STR.