MétaCan
Menu
Back to cohort
Record W2346801004 · doi:10.5271/sjweh.3567

Labor markets and health: an integrated life course perspective

2016· article· en· W2346801004 on OpenAlexafffund
Benjamin C. Amick, Chris McLeod, Ute B ltmann

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British ColumbiaInstitute for Work & Health
FundersCanadian Institutes of Health Research
KeywordsLife course approachPerspective (graphical)Work (physics)Context (archaeology)Framing (construction)Personnel economicsBusinessPublic relationsSociologyLabour economicsEconomicsPsychologyPolitical scienceLabor relationsSocial psychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Current work and health research is fragmented, focusing on jobs, exposures, specific worker groups, work organization, or employment contracts. An emphasis on the labor market in framing the work and health relationship conceptualizes work not only as an exposure that increases or lessens health risk but also as a life course experience that is dependent on place and time. The intention is to illustrate how the labor markets and health framework coupled with a life course perspective extends other epidemiological approaches to work and health to identify new research questions. Taking the changing nature of work and labor markets into account, this paper updates the labor markets and health framework. It then reviews, defines, and integrates key life course concepts. A model is developed that guides the understanding of how labor markets and health trajectories emerge from the consideration of the working life course in a social context. The application leads to new research questions investigating characteristics of labor markets and health trajectories that may lead to positive health outcomes over the life course.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.384
Teacher spread0.339 · 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.

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

Citations82
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueScandinavian Journal of Work Environment & HealthSame topicEmployment and Welfare StudiesFrench-language works237,207