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Record W2899720313 · doi:10.1093/geroni/igy023.239

OBJECTIVELY MEASURED PHYSICAL ACTIVITY ACROSS OCCUPATIONS BASED ON THE NHANES

2018· article· en· W2899720313 on OpenAlexaff
Jeremy A. Steeves, Catrine Tudor‐Locke, Rachel A. Murphy, G.A.M. King, David R. Bassett, Dane R. Van Domelen, John M. Schuna, Tamara B. Harris

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsCanadian Centre for Applied Research in Cancer ControlUniversity of British Columbia
Fundersnot available
KeywordsNational Health and Nutrition Examination SurveyPhysical activityEnvironmental healthMedicineGerontologyDemographyPopulationPhysical therapy

Abstract

fetched live from OpenAlex

Occupational activity may contribute significantly to daily physical activity (PA). The 2003–2006 National Health and Nutrition Examination Survey (NHANES) surveillance cycles provide an opportunity to evaluate the amount of daily PA in occupational categories. Population estimates of accelerometer-derived PA variables were compared for men and women (n=1112 and n=1501) working the 40 and 22 occupational categories in the 2003–2004 and 2005–2006 NHANES, and used to classify the occupational categories: high, intermediate, and low activity groupings based on total daily PA. There was a strong association between occupational category and total daily PA. Labor jobs were generally high active, while office jobs were generally low active. Men had more moderate-to-vigorous activity than women in all activity groupings. Presenting objectively measured PA from a large representative sample with a variety of occupations provides previously unquantifiable information about the daily PA levels of people employed in different occupational categories.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

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

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 head0.107
GPT teacher head0.407
Teacher spread0.301 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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