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Record W4366992484 · doi:10.1016/j.ssmph.2023.101414

Mental health and labor supply: Evidence from Canada

2023· article· en· W4366992484 on OpenAlexaboutno aff
Yichen Shen

Bibliographic record

VenueSSM - Population Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthInstrumental variableSpillover effectPsychologyDemographic economicsPsychiatryEconomicsEconometrics

Abstract

fetched live from OpenAlex

This study investigates the relationship between mental health and employment using an instrumental variable approach with the 2012 Canadian Community Health Survey-Mental Health. Using a family member's mental health problem(s) as an instrument for poor mental health, the estimates reveal that poor mental health significantly reduces employment outcomes. These findings are robust to various specifications, such as an alternative instrument and a relaxation of the exclusion restriction assumption. In addition, the relationship is driven mainly by men and younger workers. Moreover, the findings suggest that the relationship is mediated by a decline in cognitive abilities, such as difficulties in concentration and motivation, and social relations with acquaintances and friends. Finally, the estimates show that this phenomenon is contagious: poor mental health has a significant spillover effect on coworkers' mental health in workplaces. This study demonstrates the importance of mental health illness in Canada and other developed countries.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.185
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.451
Teacher spread0.355 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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