JOURNAL ARTICLE

Generational effects in self-reported age of onset for youth suicidal ideation, self-harm and attempted suicide: A retrospective analysis using data from the Australian National Study of Mental Health and Wellbeing, 2020–2022.

  • Published In: Australian & New Zealand Journal of Psychiatry, 2026, v. 60, n. 3. P. 269 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Witt, Katrina; Teo, Shu Mei; Ziou, Myriam; Rajaram, Gowri; Baker, David G; Browne, Vivienne; Schmaal, Lianne; McGorry, Patrick D; Gao, Caroline X 3 of 3

Abstract

This article examines generational differences in the prevalence and age of onset of suicidal ideation, plans, self-harm, and suicide attempts by age 25 using data from the Australian Bureau of Statistics National Study of Mental Health and Wellbeing (2020–22). The study found that Generation Z (aged 16–25) reported the highest risk and earliest onset of these outcomes compared with Millennials, Generation X, and Baby Boomers. Key risk factors such as adverse childhood experiences and lifetime mental health diagnoses were consistent across generations, though their impact varied; for example, witnessing parental violence and exposure to suicide were more strongly associated with earlier onset in Generation Z. The findings highlight the need for tailored, multi-sectoral suicide prevention strategies that address generational-specific risks and emphasize early intervention in youth populations.

Additional Information

  • Source:Australian & New Zealand Journal of Psychiatry. 2026/03, Vol. 60, Issue 3, p269
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:0004-8674
  • DOI:10.1177/00048674251393162
  • Accession Number:191833454
  • Copyright Statement:Copyright of Australian & New Zealand Journal of Psychiatry is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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