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Record W2148783024 · doi:10.1177/152483990100200213

From Theory to Practice: A Determinants Approach to Workplace Health Promotion in Small Businesses

2001· article· en· W2148783024 on OpenAlexaffabout
Joan M. Eakin, Maureen Cava, Trevor F. Smith

Bibliographic record

VenueHealth Promotion Practice · 2001
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsToronto Public HealthUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsHealth promotionWorkplace health promotionPublic relationsContext (archaeology)Public healthOccupational health nursingNursingBusinessMedicinePolitical science

Abstract

fetched live from OpenAlex

A determinants approach to workplace health promotion focuses on the sources of health and ill health in the workplace itself. Key practice requirements of such an approach include the capacity to shift focus beyond the individual to the work environment, to cross disciplinary and jurisdictional boundaries in identifying problems and solutions, to foster health promotion self-sufficiency within the workplace, to enable worker participation in the process, and to adapt practice strategies to a business setting. This article identifies the challenges of such practice by reference to the experiences of health promoters in a Canadian public health department who attempted a determinants-centered stress reduction program for small-sized businesses. Findings under-score the significance for workplace health promotion of the broader structural context in which the workplace and the intervention are located, of differing perspectives between health professionals and workplace parties, and of conflicting professional accountabilities. Possibilities for addressing these challenges are considered.

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)0.028
metaresearch head score (Gemma)0.023
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0080.040
Scholarly communication0.0180.009
Open science0.0040.010
Research integrity0.0050.007
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 head0.084
GPT teacher head0.452
Teacher spread0.368 · 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

Citations39
Published2001
Admission routes2
Has abstractyes

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