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Record W1987736720 · doi:10.5271/sjweh.698

Factors influencing the impact of unemployment on mental health among young and older adults in a longitudinal, population-based survey

2003· article· en· W1987736720 on OpenAlexaffabout
F. Curtis Breslin, Cameron Mustard

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2003
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsUnemploymentMental healthDepression (economics)WorkforcePsychologyDemographyPopulationYoung adultAssociation (psychology)Longitudinal studyGerontologyPsychological distressMedicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examined the relationship between unemployment and mental health. It particularly emphasized the potential differences in mental health status between younger workers entering the labor market and older workers with established laborforce involvement. METHODS: With the use of the National Population Health Survey in Canada, over 6000 respondents between 18 and 55 years of age in 1994 were followed up 2 years later. RESULTS: The results suggest that, among the 31- to 55-year-olds, becoming unemployed led to increases in distress and, to some extent, clinical depression at follow-up. This association between unemployment and mental health was not found among younger adults 18 to 30 years of age. Possible explanations for the null finding among young adults, such as decreased likelihood of low household income or increased likelihood of distressed young adults completely withdrawing from the workforce, were not supported. The notion that baseline mental health affects the chances of being unemployed at the time of a 24-month follow-up were partially supported. CONCLUSIONS: These findings from a representative sample suggest that both causation and selection processes lead to an association between unemployment and distress among older adults.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.045
GPT teacher head0.362
Teacher spread0.317 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations120
Published2003
Admission routes2
Has abstractyes

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