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Record W7113902733 · doi:10.1080/13676261.2025.2599913

Precarious employment and mental health among young Australians with disability: a mixed-methods study during the COVID-19 pandemic

2025· article· en· W7113902733 on OpenAlexaff

Bibliographic record

VenueJournal of Youth Studies · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsCentre for Global Health Research
FundersNational Health and Medical Research Council
KeywordsMental healthPandemicCoronavirus disease 2019 (COVID-19)Qualitative researchYoung adultUnemploymentPrecarious work

Abstract

fetched live from OpenAlex

There is increasing recognition of the challenges young people face in navigating precarious labour markets, particularly during the COVID-19 pandemic. However, the relationship between precarious employment and mental health remains underexplored, especially for young people with disability who face systemic labour market barriers. This study uses data from the Australian Youth Employment Study to: (1) examine patterns of precarious employment among young people with and without disability; (2) compare precarious employment and mental health outcomes across these groups; and (3) explore the lived experiences of young people with disability regarding precarious employment and mental health during the pandemic. Using a mixed-methods design, the study includes survey data from 112 participants (aged 17–27, mean = 21.9, 41.5% with disability) and interviews with 24 young people with psychosocial and/or intellectual disability. Results indicate that young people with disability were more likely to experience underemployment (35% versus 24% of non-disabled peers) and poorer mental health, regardless of work conditions. Interview findings reveal barriers to stable employment, exacerbated by the pandemic, leading to job instability and heightened mental health concerns. This study highlights the need for inclusive workplace practices, strengthened disability employment services, and equitable access to mental health and employment support programmes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.501
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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