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Record W7019673400

Health is Wealth: The Correlation of Wellness Programs & Productivity in Canada and the U.S.

2020· article· en· W7019673400 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - Ursinus (Ursinus College) · 2020
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPresenteeismAbsenteeismProductivityIncentiveWork (physics)Investment (military)Mental healthHealth careOccupational safety and healthWorkplace health promotion
DOInot available

Abstract

fetched live from OpenAlex

Does health impact the productivity of workers? Are there differences between the U.S. and Canada? Firms both in Canada and the U.S. deal with issues of presenteeism and absenteeism. Presenteeism is when an employee shows up to work but they are distracted by their own or a family member’s health issue. One response to reduce presenteeism and absenteeism are workplace wellness programs. Workplace wellness programs are facilitated programs by a firm to promote the health and wellbeing of their employees, which benefits the employer and the employees. There are additional incentives for U.S. employers to implement workplace wellness programs as employers are the foundation of private insurance in the U.S. while Canada operates on a one payer healthcare system. However, Canadian employers are responsible for pharmaceutical, physical therapy, and mental health insurance costs (Jacobs, 2017). Most studies examine their country and found that workplace wellness programs provide 300-400% return on investment in Canada and the U.S., making wellness programs effective and smart investments for firms to make. This study will do data analysis that will compare the effectiveness of workplace wellness programs on productivity in Canada and the U.S.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.300
Teacher spread0.272 · 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.

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
Published2020
Admission routes1
Has abstractyes

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