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Record W2156868609 · doi:10.1093/heapro/dam002

Why do managers allocate resources to workplace health promotion programmes in countries with national health coverage?

2007· article· en· W2156868609 on OpenAlexaffabout
Angela Downey, David J. Sharp

Bibliographic record

VenueHealth Promotion International · 2007
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsWestern UniversityUniversity of Lethbridge
FundersU.S. Department of Health and Human Services
KeywordsBusinessCommitIncentiveWorkplace health promotionHealth careProductivityContext (archaeology)Structural equation modelingPublic economicsHealth promotionMarketingEconomicsEconomic growth

Abstract

fetched live from OpenAlex

There is extensive evidence that worksite health promotion (WHP) programmes reduce healthcare costs and improve employee productivity. In many countries, a large proportion of healthcare costs are borne by the state. While the full benefits of WHP are still created, they are shared between employers and the state, even though the employer bears the full (after-tax) cost. Employers therefore have a lower incentive to implement WHP activity. We know little about the beliefs of managers with decision responsibility for the approval and implementation of WHP programmes in this context. This article reports the results of a study of the attitudes of Canadian senior general managers (GMs) and human resource managers (HRMs) in the auto parts industry in Ontario, Canada towards the consequences of increasing discretionary spending on WHP, using Structural Equation Modelling and the Theory of Planned Behaviour. We identified factors that explain managers' intentions to increase discretionary spending on wellness programmes. While both senior GMs and HRMs are motivated primarily by their beliefs that WHP reduces indirect costs of health failure, GMs were also motivated by their moral responsibility towards employees (but surprisingly HRMs were not). Importantly, HRMs, who usually have responsibility for WHP, felt constrained by a lack of power to commit resources. Most importantly, we found no social expectation that organizations should provide WHP programmes. This has important implications in an environment where the adoption of WHP is very limited and cost containment within the healthcare system is paramount.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.404
Teacher spread0.370 · 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 designNot applicable
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

Citations62
Published2007
Admission routes2
Has abstractyes

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