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Record W2774990822 · doi:10.1097/jom.0000000000001240

The Measurable Benefits of a Workplace Wellness Program in Canada

2017· article· en· W2774990822 on OpenAlexaffabout
Ilka Lowensteyn, Violette Berberian, Patrick Bélisle, Deborah DaCosta, Lawrence Joseph, Steven A. Grover

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineMental healthPhysical therapyBlood pressurePhysical activityGerontologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to evaluate the impact of an employee wellness program in Canada. METHODS: A comprehensive program including web-based lifestyle challenges was evaluated with annual health screenings. RESULTS: Among 730 eligible employees, 688 (94%) registered for the program, 571 (78%) completed a health screening at baseline, and 314 (43%) at 1 year. Most (66%) employees tracked their activity for more than 6 weeks. At 1-year follow-up, there were significant clinical improvements in systolic blood pressure -3.4 mm Hg, and reductions in poor sleep quality (33% to 28%), high emotional stress (21% to 15%), and fatigue (11% to 6%). A positive dose-response was noted where the greatest improvements were observed among those who participated the most. CONCLUSION: The program had high employee engagement. After 1 year, the benefits included clinically important improvements in physical and mental 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 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 score0.050
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.355
Teacher spread0.318 · 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

Citations37
Published2017
Admission routes2
Has abstractyes

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