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Record W2325231087 · doi:10.1097/jom.0b013e318222af67

The Healthy LifeWorks Project

2011· article· en· W2325231087 on OpenAlexafffund
Lydia Makrides, Steven M. Smith, Jane Allt, Jane Farquharson, Claudine Szpilfogel, Sandra Curwin, Paula Veinot, Feifei Wang, Dee Edington

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2011
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsSaint Mary's UniversityStatistics CanadaHeart and Stroke FoundationDalhousie University
FundersPfizer CanadaPfizer
KeywordsAbsenteeismMedicineEnvironmental healthWilcoxon signed-rank testRisk assessmentInternal medicinePsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the relationship between health risks and absenteeism and drug costs vis-a-vis comprehensive workplace wellness. METHODS: Eleven health risks, and change in drug claims, short-term and general illness calculated across four risk change groups. Wellness score examined using Wilcoxon test and regression model for cost change. RESULTS: The results showed 31% at risk; 9 of 11 risks associated with higher drug costs. Employees moving from low to high risk showed highest relative increase (81%) in drug costs; moving from high to low had lowest (24%). Low-high had highest increase in absenteeism costs (160%). With each risk increase, absenteeism costs increased by $CDN248 per year (P < 0.05) with average decrease of 0.07 risk factors and savings $CDN6979 per year. CONCLUSIONS: Both high-risk reduction and low-risk maintenance are important to contain drug costs. Only low-risk maintenance also avoids absenteeism costs associated with high risks.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0700.014

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 head0.079
GPT teacher head0.399
Teacher spread0.320 · 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

Citations19
Published2011
Admission routes2
Has abstractyes

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