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Record W2566082029 · doi:10.1097/jom.0000000000000906

Male-Female Differences in Work Activity Limitations

2016· article· en· W2566082029 on OpenAlexaboutno aff
Kathy Padkapayeva, Cynthia Chen, Amber Bielecky, Selahadin Ibrahim, Cam Mustard, Dorcas Beaton, Peter Smith

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical activityWork (physics)Diabetes mellitusOccupational safety and healthChronic diseaseMoodGerontologyDemographyPsychologyClinical psychologyPhysical therapyInternal medicineEndocrinologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine differences in activity limitations at work among men and women, and the relative contributions that chronic conditions and occupational characteristics have on these differences. METHODS: Secondary data from the Canadian Community Health Surveys were used. Path analysis examined the role of mediating variables (chronic conditions and occupational characteristics) in male-female differences in work activity limitations. RESULTS: The prevalence of activity limitations at work was higher in women (15.0%) than in men (12.3%). Arthritis, migraines, diabetes, heart disease, and mood disorders, as well as high physical demands and prolonged standing were associated with an increased risk of work activity limitations. The increased risk of work activity limitations among women was completely explained by mediating variables. CONCLUSIONS: This study suggests that male-female differences in work activity limitations can be explained by differences in chronic conditions and occupational characteristics.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.094
GPT teacher head0.374
Teacher spread0.280 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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