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Record W2823305131 · doi:10.5430/ijba.v9n4p103

Work Health and Safety in Small Business-A Pilot Study in the Australian Construction Industry

2018· article· en· W2823305131 on OpenAlexvenueno aff
Raed Eldejany

Bibliographic record

VenueInternational Journal of Business Administration · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational safety and healthDescriptive statisticsWork (physics)BusinessSmall businessMarketingConstruction industryWelfareOperations managementEngineeringEconomicsMedicineConstruction engineering

Abstract

fetched live from OpenAlex

Work accidents impacts negatively on the physical, mental and social welfare of employees, increase cost of production, and make firms less competitive. The construction industry in Australia consists of 96% small business and has the fifth largest incident rates of serious injury of all industries. Nevertheless, recent statistics by the Australian Bureau of Statistics show noticeable improvement in safety performance within the construction industry compared to previous years.This descriptive pilot study attempts to verify small business contribution to this recent improvement. Ten owner managers are surveyed in order to examine their commitment to work health and safety using a 34 self-completion questionnaire. The findings show that small business owners in the construction industry take a positive approach toward work health and safety in their work environment.This study represents only a snapshot of the reality of small construction business commitment to work health and safety in Australia and can’t be generalised to a wider population, therefore further research with larger samples is required to confirm the findings of this study.

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)0.003
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 score0.041
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.182
GPT teacher head0.486
Teacher spread0.304 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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