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Record W4289521075 · doi:10.21203/rs.3.rs-1910286/v1

Health, Mental Health, and Time Use: A Canadian Study

2022· preprint· en· W4289521075 on OpenAlexaffabout
Ehsan Latif

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsTobit modelMental healthPhysical healthSample (material)PsychologyWork (physics)Leisure timeGerontologyPhysical activityDemographyPsychiatryMedicineEconomicsSociologyEconometricsPhysical therapy

Abstract

fetched live from OpenAlex

Abstract Using data from Canadian General Social Survey (Time Use- 2015), the study examined how physical health and mental health are related to individuals' time use in sleep, personal care, non-market work, market work, and leisure. The study used Seemingly Unrelated Regression (SUR) and Tobit methods to estimate models. The study found that physical and mental health are negatively correlated with market hours and positively correlated with sleep and leisure. These results are consistent for both the male sample and the female sample. The study also focused on prime-age individuals and found that physical health and mental health significantly reduced time spent on work and significantly increased leisure time for both males and females.

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)0.003
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: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0100.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.120
GPT teacher head0.459
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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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