MétaCan
Menu
Back to cohort
Record W2006655992 · doi:10.1017/s071498080000372x

Later-Life Career Disruption and Self-Rated Health: An Analysis of General Social Survey Data

2003· article· en· W2006655992 on OpenAlexaffabout
Yunzhao He, Angela Colantonio, V. Marshall

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJob lossGeneral Social SurveyPsychologyGerontologySelf-rated healthCausationDemographyDemographic economicsMedicineSocial psychologySociologyPolitical scienceUnemploymentEconomics

Abstract

fetched live from OpenAlex

ABSTRACT The transition from employment to retirement is changing dramatically in Canada and other industrialized societies, with a decreasing proportion of working life being spent in stable career progression. This study used a sample of 2,592 subjects, aged 45 to 64, from the 1994 General Social Survey of Canada (GSS): Cycle 9, to describe situations of later-life career disruption (LLCD) in older workers in Canada and to investigate the association between LLCD and self-rated health. Results showed that a large proportion of older Canadian workers had experienced such LLCD as job interruption and job loss. Experience of job loss and job interruption over the prior 5-year period was found to be significantly associated with poor self-rated health, after controlling for age, education, body mass index, and activity limitation. However, after excluding respondents whose LLCD was known to be due to poor health, job interruption and job loss were separately found not to be significantly associated with poor health. The complexity of the findings and the direction of causation between LLCD and self-rated health, as well as some methodology issues, are discussed. Areas of future research are indicated.

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)0.001
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 score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.152
GPT teacher head0.365
Teacher spread0.213 · 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

Citations10
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicRetirement, Disability, and EmploymentFrench-language works237,207