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Record W2319140089 · doi:10.1080/10803548.2016.1158591

Facilitators and barriers to occupational health and safety in small and medium-sized enterprises: a descriptive exploratory study in Ontario, Canada

2016· article· en· W2319140089 on OpenAlexaffabout
Behdin Nowrouzi‐Kia, Basem Gohar, Martyna Garbaczewska, Olena Chapovalov, Étienne Myette‐Côté, Lorraine Carter

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsNipissing UniversityUniversity of AlbertaCanadian Public Health AssociationUniversity of TorontoLaurentian University
Fundersnot available
KeywordsOccupational safety and healthBusinessLegislationPrivate sectorSafety culturePsychological interventionPublic sectorExploratory researchHealth careEffective safety trainingWork (physics)Focus groupEnvironmental healthPublic healthNursingMedicineMarketingOccupational health nursingHealth policyEngineeringEconomic growthPolitical scienceManagementSociology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this particular study was to test a newly created instrument in describing the facilitators and barriers to occupational health and safety in small and medium-sized enterprises (SMEs) in Ontario, Canada. METHODS: A cross-sectional design was used to identify the occupational health and safety culture of SMEs in public and private sectors in Ontario. RESULTS: A total of 153 questionnaires were completed. The majority of respondents were female (84%) with a mean age of 49.8 years (SD 10.6). Seventy-four percent were supervisors. Seventy percent of respondents were from the private sector while 30% derived from the public sector including healthcare, community services, and non-profit organizations. Further, conducting regular external safety inspections of the workplace was found to be statistically associated with a safe work environment 2.88 95% CI [1.57, 5.27]. CONCLUSIONS: Strategies and training opportunities that focus on how to adapt occupational health and safety legislation to the nature and diversity of SMEs are recommended. Furthermore, employers may use such information to improve safety in their SMEs, while researchers can hopefully use such evidence to develop interventions that are applicable to meeting the occupational health and safety needs of SMEs.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.680
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.064
GPT teacher head0.381
Teacher spread0.317 · 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.

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

Citations27
Published2016
Admission routes2
Has abstractyes

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