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Record W4298127463 · doi:10.1016/j.ssmph.2022.101238

Time availability as a mediator between socioeconomic status and health

2022· article· en· W4298127463 on OpenAlexaboutno aff
Boróka Bó

Bibliographic record

VenueSSM - Population Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersNeurosciences FoundationUniversity of California BerkeleyPaul and Daisy Soros Fellowships for New AmericansNational Science Foundation
KeywordsScarcityMediationSocioeconomic statusSample (material)Time allocationPovertySet (abstract data type)Logistic regressionPsychologyEnvironmental healthSurvey data collectionDemographic economicsMedicineEconomicsStatisticsSociologyEconomic growthPopulationComputer scienceMathematics

Abstract

fetched live from OpenAlex

This study shows that time availability is a significant mediator between SES and health. I draw on representative survey data from the Canadian Multinational Time Use Survey and supplement this data source with a second data set containing localized sociodemographic and time availability measures. In addition to testing existing time scarcity measures, I also propose a broader set of new, more inclusive measures. Analyses involve two stages. First, binary logistic regressions evaluate statistically significant relationships. The second stage uses mediation analyses to assess whether time availability is statistically significant in mediating the relationship between SES and self-reported health. I compute direct, indirect, and total effects, independently for each of the objective and subjective time availability measures, for both the nationally representative sample and for the localized sample. My results show that both time scarcity and time excess are important when examining the mechanisms linking SES and health. For example, 12 percent of the effect of household-level SES on health is via discretionary time availability. Further, over 10 percent of the effect of neighborhood-level SES on health is via subjective time scarcity. Objective time poverty mediates about 9 percent. 7.3 percent of the effect of SES on health is via objective time excess. Considering the differing temporal needs of marginalized populations, this work has important health policy implications for sociotemporal disparities in 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.394
Teacher spread0.348 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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