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Record W2585204901 · doi:10.1111/roiw.12293

Does Economic Insecurity Cause Weight Gain Among Canadian Labor Force Participants?

2017· article· en· W2585204901 on OpenAlexaffabout
Barry Watson

Bibliographic record

VenueReview of Income and Wealth · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsEconomicsPopulation healthBody mass indexDemographic economicsObesityIndex (typography)PopulationCausality (physics)DemographyRecessionConsumer confidence indexMedicineEnvironmental healthHealth careEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

The National Population Health Survey (NPHS) suggests that for labor force participants age 25 to 64, the prevalence of self‐reported obesity in Canada has increased from 16 percent in 1998 to 23 percent in 2008. Using six cycles of NPHS data (1998–2009), I explore Canada's obesity dilemma by considering the effect of economic insecurity—measured as the probability of an individual experiencing a severe negative economic shock. As an identification strategy, a fixed effects model is employed to control for unobserved time‐invariant heterogeneity and a set of instruments based on an individual's economic environment are specified in order to isolate causality. Results suggest that for males age 25 to 64, a 1 percent increase in economic insecurity is predicted to increase their body mass index (BMI) by 0.10 points. For females age 25 to 64, the association between economic insecurity and BMI is statistically insignificant at conventional confidence levels.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.031
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.408
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2017
Admission routes2
Has abstractyes

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