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Record W2592402641 · doi:10.1177/0022146517696148

Insecure People in Insecure Places: The Influence of Regional Unemployment on Workers’ Reactions to the Threat of Job Loss

2017· article· en· W2592402641 on OpenAlexafffundabout
Paul Glavin, Marisa Young

Bibliographic record

VenueJournal of Health and Social Behavior · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsUnemploymentMental healthJob insecurityJob lossPsychologyMultilevel modelPerceptionDemographic economicsSocial psychologyEconomicsWork (physics)Economic growthPsychiatry

Abstract

fetched live from OpenAlex

Social comparison theory predicts that unemployment should be less distressing when the experience is widely shared, but does this prediction extend beyond the unemployed to those who are at risk of job loss? Research demonstrates a link between aggregate unemployment and employed individuals' perceptions of job insecurity; however, less is known about whether the stress associated with these perceptions is shaped by others' unemployment experiences. We analyze a nationally representative sample of Canadian workers (Canadian Work, Stress, and Health study; N = 3,900) linked to census data and test whether regional unemployment influences the mental health consequences of job insecurity. Multilevel analyses provide more support for the social norm of insecurity hypothesis over the amplified threat hypothesis: the health penalties of job insecurity are weaker for individuals in high-unemployment regions. This contingency is partially explained by the ability of insecure workers in poor labor market contexts to retain psychological resources important for protecting mental health.

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 score0.089
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 integrity0.0000.001
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 head0.096
GPT teacher head0.440
Teacher spread0.344 · 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

Citations26
Published2017
Admission routes3
Has abstractyes

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