MétaCan
Menu
Back to cohort
Record W2609720518 · doi:10.3390/ijerph14050470

Does Unstable Employment Have an Association with Suicide Rates among the Young?

2017· article· en· W2609720518 on OpenAlexaff
Chungah Kim, Youngtae Cho

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcMaster University
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsUnemploymentPer capitaEmployment protection legislationDemographyProxy (statistics)EconomicsDemographic economicsSuicide preventionPoison controlMedicineEnvironmental healthPopulationEconomic growthSociology

Abstract

fetched live from OpenAlex

Although a growing body of literature has indicated that unemployment has a positive association with suicide, the dynamic aspects of unstable employment have not yet been considered in suicidology. This study explored the association between employment stability and completed suicide among people aged 25-34 years in 20 OECD (Organization for Economic Cooperation and Development) countries with time-series data (1994-2010). In order to consider the different aspects of unstable employment, we tested the impacts of employment protection legislation indicators as another proxy of job insecurity (employed, but unstable) apart from unemployment rates. Covariates, including economic growth rates, GDP per capita, fertility rates, and divorce rate, were controlled for. The analysis was designed to be gender- and age-specific, where observations with ages of 25-29 were separated from those with ages of 30-34. Random effect models were applied to examine changes over time in suicide rates, and other models were presented to check robustness. The results showed that it is a low level of employment protection, rather than unemployment itself, that was associated with increased suicide rates among all of the studied populations. The magnitude of the effect differed by gender.

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)0.006
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 score0.100
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.114
GPT teacher head0.477
Teacher spread0.363 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicEmployment and Welfare StudiesFrench-language works237,207