Navigating housing independence: transitions out of the parental home of young Australians with and without disability
Bright, T.; Bishop, G.; Mason, K.; Sully, A.; Gurrin, D.; Dickinson, H.; Kavanagh, A.; Aitken, Z.
Show abstract
Young people are increasingly remaining in the parental home for longer - a trend associated with poorer mental health. There is little evidence on this transition for young people with disability. We used three waves of the Australian Census Longitudinal Dataset, a 5% sample of linked Census records. Two analyses compared transitions between 2011-2016 and 2016-2021 among people 15-34y living with parents at baseline with complete data on disability and housing. The proportion of people no longer living with parents at follow-up was calculated, comparing people with and without disability, along with absolute and relative inequalities. Young people with disability were half as likely to leave the parental home as their peers without disability. Inequalities were greatest for people 25-29y (relative difference 0.41 (95%CI 0.36-0.45), living outside major cities (0.48, 0.44-0.52), or with higher income (0.53 (0.47-0.59). Patterns were consistent over time. Targeted supports are needed to enable independent living. Points of interestO_LIWe found that less people with disability leave the parental home than people without disability C_LIO_LIWe also found the gap between people with and without disability was biggest outside major cities. C_LIO_LIThis may mean people with disability in rural, regional and remote areas find it more difficult to move out of home C_LIO_LIBetter housing and income supports are needed to help young people with disability live in the way they choose C_LI
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The health impacts of a 4-month long community-wide COVID-19 lockdown: Findings from a prospective longitudinal study in the state of Victoria, Australia 94%
- Intentional and unintentional non-adherence to social distancing measures during COVID-19: A mixed-methods analysis 94%
- Loneliness among people with severe mental ill health during the COVID-19 pandemic: results from a linked UK population cohort study 94%
Similar papers in this journal
- Psychological, social and financial impact of COVID-19 on culturally and linguistically diverse communities: a cross-sectional Australian study 95%
- Examining the intersection of child protection and public housing: development, health and justice outcomes using linked administrative data 95%
- Vulnerabilities in child wellbeing among primary school children: a cross-sectional study in Bradford, UK 93%
Similar papers in this journal
- Acceptability and feasibility of strategies to shield the vulnerable during the COVID-19 outbreak: a qualitative study in six Sudanese communities 93%
- Physical Activity Behaviour in Middle-Aged and Older Canadian Women and Men: An Analysis of the CLSA 93%
- How is the COVID-19 pandemic impacting our life, mental health, and well-being? Design and preliminary findings of the pan-Canadian longitudinal COHESION Study 92%
Similar papers in this journal
- COVID-19 infection and outcomes in a population-based cohort of 17,173 adults with intellectual disabilities compared with the general population 94%
- Characteristics of those most vulnerable to employment changes during the Covid-19 pandemic: a nationally representative cross-sectional study in Wales 94%
- Long COVID and financial outcomes: Evidence from four longitudinal population surveys 92%
Similar papers in this journal
- Challenges to self-isolation among contacts of cases of COVID-19: a national telephone survey in Wales 95%
- Does household income predict health and educational outcomes in childhood better than neighbourhood deprivation? 93%
- Understanding patterns of adherence to COVID-19 mitigation measures: A qualitative interview study 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.