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Record W2612679361 · doi:10.5539/ach.v9n2p1

Shaping the Culture of Safety through Effective Leadership in Malaysia

2017· article· en· W2612679361 on OpenAlexvenueno aff
Fanny Yam, Chih Siong Wong, Cheah Yuat Hoong, Mansoureh Ebrahimi

Bibliographic record

VenueAsian Culture and History · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsSafety cultureOrganizational culturePublic relationsLeadership stylePsychologyManagementPolitical science

Abstract

fetched live from OpenAlex

Despite the enforcement of safety protocols, several workplaces and organizations still face accidents in Malaysia. SOSCO reported >34,000 workplace related accidents in 2012, 983 of which were fatal. Leadership is important when creating a culture that supports and promotes health and safety. Management and Team leaders are vital in inspiring workers to higher levels of safety consciousness and productivity, which means they must personally apply good leadership attributions daily. A ‘Safety Culture’ describes a safety management style in the workplace that reflects attitudes, beliefs, perceptions and values shared by all workers with regard to safety. The objectives of this study include raising the awareness among leaders in the workplace of their role and responsibility in the mitigation and construction of a safety culture that approaches zero incidents in the workplace. The methodology used in this paper includes a qualitative literature research on safety culture and leadership in addition to a quantitative survey that focused on safety culture at two Malaysian universities. This research thus provides an in-depth analysis and platform for organizations to identify areas of weakness and concern and can lead to further research that builds on existing systems to strengthen safety culture awareness and praxis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.977
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.227
GPT teacher head0.437
Teacher spread0.210 · 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
Published2017
Admission routes1
Has abstractyes

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