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Record W2162810462 · doi:10.5539/ass.v9n3p182

Safety Culture in Combating Occupational Safety and Health Problems in the Malaysian Manufacturing Sectors

2013· article· en· W2162810462 on OpenAlexvenueno aff
Noor Aina Amirah, Wan Izatul Asma, Mohd Shaladdin Muda, Wan Abd Aziz Wan Mohd Amin

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational safety and healthBusinessLegislationSafety cultureEffective safety trainingIndustrialisationManufacturingWorkplace safetyQuality (philosophy)Operations managementRisk analysis (engineering)MarketingOccupational health nursingEngineeringHealth careEconomic growthMedicineEconomicsHealth policyManagement

Abstract

fetched live from OpenAlex

Rapid economic growth via industrialization has given not only a significant impact in terms of income distributions and quality of life, but it also resulted in increasing number of accidents at workplace. In reducing risk at the workplace, Occupational Safety and Health (OSH) is an important aspect. It is a standard which are set in legislation with the aim to eliminate and reduce hazards at workplace. Besides OSH, the term of ‘safety culture’ is also an important aspect in reducing risk and accident at workplace. This paper highlights the problems in the Malaysian manufacturing industries namely the high accident rate in manufacturing industries which may be due to lack of safety culture and non-compliance of the Malaysian Occupational Safety and Health Act’s (OSHA’s) requirements which may inadvertently led to lack of safety culture. The existence of these problems shows that workers’ and employers’ behaviour and compliance to OSHA will lead to positive safety culture which in turn will lead to reduction of accidents rate in the manufacturing industries in Malaysia.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.069
GPT teacher head0.443
Teacher spread0.375 · 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 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
Published2013
Admission routes1
Has abstractyes

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