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Record W3106726137 · doi:10.1097/jom.0000000000002087

Organizational Perspectives on How to Successfully Integrate Health Promotion Activities into Occupational Health and Safety

2020· article· en· W3106726137 on OpenAlexaff
Aviroop Biswas, Momtaz Begum, Dwayne Van Eerd, Peter Smith, Monique A. M. Gignac

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsOccupational safety and healthHealth promotionOccupational health nursingWorkplace health promotionEnvironmental healthPromotion (chess)Occupational medicineBusinessMedicinePublic healthNursingOccupational exposurePolitical sciencePolitics

Abstract

fetched live from OpenAlex

OBJECTIVE: There is increasing recognition of the value of integrating efforts to promote worker health with existing occupational health and safety activities. This paper aimed to identify facilitators, barriers and recommendations for implementing integrated worker health approaches. METHODS: Thirteen stakeholders from different job sectors participated in a workshop that targeted key issues underlying integrated worker health approaches in their own and other organizations. Included were participants from human resources, occupational health and safety, government, and unions. Thematic analysis and an online ranking exercise identified recommendation priorities and contributed to a conceptual framework. RESULTS: Participants highlighted the importance of planning phases in addition to implementation and evaluation. Themes highlighted organizational priorities, leadership buy-in, external pressures, training, program promotion and evaluation metrics. CONCLUSIONS: Findings provide practical directions for integrating worker health promotion and safety and implementation steps.

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)0.052
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0090.008
Scholarly communication0.0120.005
Open science0.0020.010
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.072
GPT teacher head0.417
Teacher spread0.344 · 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 designQualitative
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

Citations20
Published2020
Admission routes1
Has abstractyes

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