Despite acknowledged demands and stresses associated with early childhood (EC) work, and high levels of attrition in the EC workforce, research often shows high ratings of job satisfaction. This article explores these discordant findings by applying Job Demands-Resources (JD-R) Model to explore educator-rated job satisfaction, stress, feeling rushed, and multiple demands using Time Use Diary (TUD) methodology in a sample of well-resourced EC centres. TUD records were completed by 321 educators for 20 randomly selected hours over 10 days of work, generating 3,512 h of data. Overall ratings were high (M = 7.2/10) for satisfaction and low (M = 3.8/10) for job demands, with variations across the day, and by type and number of work activities completed per hour. Demands reduced during ‘staff breaks’ and time spent ‘being with children’, and increased when ‘communicating with families’, providing ‘emotional support’, and other tasks. Although overall satisfaction was negatively correlated with demands (r = − 0.40), relatively high ratings were maintained during periods of high job demands. Applying TUD methodology to the JD-R model provided more nuanced understandings of work demands than is possible through one-off surveys. Findings have important implications for supporting quality EC practice and reducing educator burnout and attrition.

Despite the acknowledged complexity and time pressures of early childhood educators’ work, very few studies have examined the nature of this work, minute-by-minute, over the working day. This paper reports on data gathered through 10,155 time-use diary (TUD) records provided by 321 educators participating in the Exemplary Early Childhood Educators at Work Australian Research Council Linkage Project. Participants were recruited from preschool/kindergarten and long day care centres that had achieved a rating of Exceeding the Australian National Quality Standard on all seven Quality Areas. Analyses of this extensive dataset illustrate the rhythm and diversity of educators’ work across a typical day and identify the similarities and differences in worktime distributions for educators working in preschool vs. long day care settings, and for educators with different qualifications and positional responsibilities. The findings suggest differential allocations of worktime that raise important considerations for achieving high quality early childhood education and care services.

Despite the acknowledged complexity of early childhood educators' work, little is documented about how early childhood educators actually spend their time at work. A typical way of studying time at work is through the use of time-use diaries. Recent developments have shown the benefits of using randomized sampling electronic time-use diaries. This paper reports on the development and useability testing of a random time sampling (RTS) time-use smart-phone application to capture the work of educators, the first time such a method has been used in early childhood settings. Descriptive analyses were conducted of time use data collected from 20 Australian early childhood educators. Seventeen went on to participate in follow-up focus groups / interviews, which were thematically analysed. The paper demonstrates the capacity of RTS apps to gather accurate and useful data about educators' work, and points to the acceptability of this method to educators, and its manageability within early childhood settings. Limitations of the method, including, participant buy-in, design and technical requirements, and cost, are also highlighted.

The COVID-19 pandemic has drawn attention to the care economy, including commodified early childhood education and care (ECEC). While there is some literature about the low paid, invisible, and undervalued skills among the predominantly female workforce in the ECEC sector, there is little research into what these educators do in their working day and how this contributes to quality education and care for young children. This article provides a detailed examination of ten defined domains of ECEC work tasks, derived from data generated by educators’ use of ‘intensive hour’ time-diary methodology. The results reveal that the outstanding characteristics of this occupation are multi-tasking and the rapid switching of tasks as educators manage diverse expectations arising from work with groups of very young children, families, other staff, and meeting legislated responsibilities. Drawing on William J. Baumol’s economic theory, we consider the implications for productivity and cost tensions in ECEC.

List of tables and figures..List of abbreviations..Acknowledgements..Preface..1 Is the myth of the nuclear family dead?..2 The other life of the family..3 The rise of intimacy..4 Working for nothing..5 At home: the more things change, the more they. stay the same..6 Pseudomutuality: the disjunction between domestic. inequality and the ideal of equality..7 Economics, breadwinning and family relations..8 How the family is a problem for the state..9 The greatest welfare system ever devised?..Notes..References..Index

This section defined time use (TU) research, illustrating its relevance for public health. TUR in the health context is the study of health-enhancing and health-compromising behaviours that are assessed across a 24 h day. The central measurement is the use of Time Use Diaries, which capture 24–48 h, typically asking about behaviour in each 15-min period. TUR is used for understanding correlates of health behaviours, and as a form of population surveillance, assessing behavioural trends over time. This paper is a narrative review examining the history of time use research, and the potential uses of TU data for public health research. The history of TUR started in studies of the labour force and patterns of work in the late 19th and early twentieth century, but has more recently been applied to examining health issues. Initial studies had a more economic purpose but over recent decades, TU data have been used to describe the distribution and correlates of health-enhancing patterns of human time use. These studies require large multi-country population data sets, such as the harmonised Multinational Time Use Study hosted at the University of Oxford. TU data are used in physical activity research, as they provide information across the 24-h day, that can be examined as time spent sleeping, sitting/standing/light activity, and time spent in moderate-vigorous activities. TU data are also used for sleep research, examining eating and dietary patterns, exploring geographic distributions in time use behaviours, examining mental health and subjective wellbeing, and examining these data over time. The key methodological challenge has been the development of harmonised methods, so population TU data sets can be compared within and between-countries and over time. TUR provides new methods for examining public health research questions where a temporal dimension is important. These time use surveys have provided unique data over decades and in many countries that can be compared. They can be used for examining the effects of some large public health interventions or policies within and between countries.

BackgroundOver the last 150years, advanced economies have seen the burden of disease shift to non-communicable diseases. The risk factors for these diseases are often co-morbidities associated with unhealthy weight. The prevalence of overweight/obesity among adults in the advanced countries of the English-speaking world is currently more than two-thirds of the adult population. However, while much attention has concentrated on changes in diet that might have provoked this rapid increase in unhealthy weight, changes in patterns of eating have received little attention.MethodsThis article examines a sequence of large-scale, time use surveys in urban Australia stretching from 1974 to 2006. The earliest survey in 1974 (conducted by the Cities Commission) was limited to respondents aged between 18 and 69years, while the later surveys (by the Australian Bureau of Statistics) included all adult (15years of age or over) living private dwellings. Since time use surveys capture every activity in a day, they contain much information about mealtimes and the patterns of eating. This includes duration of eating, number of eating occasions and the timing of eating. Inferential statistics were used to test the statistical significance of these changes and the size of the effects.ResultsThe eating patterns of urban Australian adults have changed significantly over a 32-year period and the magnitude of this change is non-trivial. Total average eating time as main activity has diminished by about a third, as have eating occasions, affecting particularly luncheon and evening meals. However, there is evidence that eating as secondary activity that accompanies another activity is now almost as frequent as eating at mealtimes. Moreover, participants seem not to report it.ConclusionsContemporary urban Australians are spending less time in organized shared meals. These changes have occurred the over same period during which there has been a public health concern about the prevalence of unhealthy weight. Preliminary indications are that societies that emphasize eating as a commensal, shared activity through maintaining definite, generous lunch breaks and prioritizing eating at mealtimes, achieve better public health outcomes. This has implications for a strategy of health promotion, but to be sure of this we need to study countries with these more socially organized eating patterns.

This working paper describes a new software application for smartphones, designed to gather timeuse data about the working day. Called random time sampling (RTS), the software was developed to overcome a void in the standard time-use survey data: namely, the lack of detail about activities undertaken and their social context during paid employment. The RTS system addresses the twin problems of (1) respondent burden, and (2) respondents providing potentially damaging information. Compared to a conventional time-diary which asks for an exhaustive recall of activities over 24 hours, the RTS only samples one hour of employment time per notification, with typically only a few notifications per working day, over a maximum of a few weeks. After becoming familiar with what is required, most respondents spend less than 90 seconds on each notification. Respondents are protected against ‘self-incrimination’ because the sampling aims to represent patterns typical of an occupation not of an individual. RTS collects insufficient information from any one individual to provide a useable measure of individual performance. The RTS system can be customised. It can be used the study the length of the (paid) workweek, the allocation of time to (99999) subtasks, the timing of the tasks (by season, by day of the week and time of day), the social context and location of these activities, and the self-rated experience of doing these employmentrelated activities. Data from pilot studies, undertaken so far, illustrates how this done.

Objectives: The aim of this study is to use national Australian time-diary data to examine both (1) crosssectionally and (2) longitudinally whether being late versus early to sleep or wake is associated with poorer child behavior, quality of life, learning, cognition and weight status, and parental mental health.Methods: Design/setting: Data from the first three waves of the Longitudinal Study of Australian Children were taken. Participants: A national representative sample of 4983 4-5-year-olds, recruited in 2004 from the Australian Medicare database and followed up biennially, was taken; 3631 had analyzable sleep information and a concurrent measure of health and well-being for at least one wave. Measures: Exposure: Parents completed 24-h child time-use diaries for one week and one weekend day at each wave. Using median splits, sleep timing was categorized into early-to-sleep/early-to-wake (EE), early-to-sleep/late-to-wake (EL), late-to-sleep/early-to-wake (LE), and late-to-sleep/late-to-wake (LL) at each wave.Outcomes: The outcomes included parent-reported child behavior, health-related quality of life, maternal/paternal mental health, teacher-reported child language, literacy, mathematical thinking, and approach to learning. The study assessed child body mass index and girth.Results: (1) Using EE as the comparator, linear regression analyses revealed that being late-to-sleep was associated with poorer child quality of life from 6 to 9 years and maternal mental health at 6-7 years. There was inconsistent or no evidence for associations between sleep timing and all other outcomes. (2) Using the count of the number of times (waves) at which a child was categorized as late-to-sleep (range 0-3), longitudinal analyses demonstrated that there was a cumulative effect of late-to-sleep profiles on poorer child and maternal outcomes at the child age of 8-9 years.Conclusions: Examined cross-sectionally, sleep timing is a driver of children's quality of life and maternal depression. Examined longitudinally, there appears to be cumulative and adverse relationships between late-to-sleep profiles and poorer child and maternal outcomes at the child age of 8-9 years. Understanding how other parameters -such as scheduling consistency, sleep efficiency and hygiene -are also related to child and parent outcomes will help health professionals better target sleep management advice to families. (C) 2016 Elsevier B.V. All rights reserved.

Aim: Using national Australian time-diary data, we aimed to empirically determine sleep duration thresholds beyond which children have poorer health, learning, quality of life, and weight status and parents have poorer mental health.Methods: Design/Setting: Cross-sectional data from the first three waves of the Longitudinal Study of Australian Children. Participants: A nationally representative sample of 4983 4-5-year-olds, recruited in 2004 from the Australian Medicare database and followed biennially; 3631 had analyzable sleep information and a concurrent measure of health and well-being for at least one wave.Main measures: Exposure: At each wave, a parent completed 24-h time-use diaries for one randomly selected weekday and one weekend day, including a "sleeping/napping" category. Outcomes: Parent-reported child mental health, health-related quality of life, and maternal/paternal mental health; teacher-reported child language, literacy, mathematical thinking, and approach to learning; and assessed child body mass index and girth.Results: Linear regression analyses revealed weak, inconsistent relationships between sleep duration and outcomes at every wave. For example, children with versus without psychosocial health-related quality of life problems slept slightly less at 6-7 years (adjusted mean difference 0.12 h; 95% confidence interval 0.01-0.22, p = 0.03), but not at 4-5 (0.00; -0.10 to 0.11, p = 1.0) or 8-9 years (0.09; -0.02 to 0.22, p = 0.1). Empirical exploration using fractional polynomials demonstrated no clear thresholds for sleep duration and any adverse outcome at any wave.Conclusions: Present guidelines in terms of children's short sleep duration appear misguided. Other parameters such as sleep timing may be more meaningful for understanding optimal child sleep. (C) 2015 Elsevier B.V. All rights reserved.