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Record W2057658312 · doi:10.5430/ijba.v5n4p12

Good Housekeeping - A Panacea for Slips, Trips & Falls Accident in the NLNG Project, Bonny

2014· article· en· W2057658312 on OpenAlexvenueno aff
Mba Okechukwu Agwu, Samuel Oluwadare Ajayi

Bibliographic record

VenueInternational Journal of Business Administration · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsHousekeepingPanacea (medicine)WorkforceOperations managementDescriptive statisticsBusinessEngineeringStatisticsMedicineMathematicsEconomicsEconomic growth

Abstract

fetched live from OpenAlex

The paper discussed good housekeeping- a panacea for slips, trips &falls (STF) accident in the NLNG project Bonny. Good housekeeping entails cleaning and orderly arrangement of materials in the workplace. The study assumes that good housekeeping practices will eliminate workplace clutter (unsafe condition) which is the common cause of STF accident and make the workplace neat, comfortable and pleasant. The research question ascertained the extent at which elimination of workplace clutter (unsafe condition) and reduction of STF accident rate is influenced by the implementation of good housekeeping programme in the NLNG project Bonny. A descriptive research design was used in conducting the study; using questionnaire administered on 384 randomly selected employees of the ten NLNG construction companies in Bonny Island. Data collected were analyzed using descriptive statistics. The results indicated that implementation of good housekeeping programme to a large extent eliminated workplace clutter and reduced STF accident rate in the NLNG project Bonny. The study therefore recommends among others: management leadership and commitment on good housekeeping, regular housekeeping audits, regular training of employees on good housekeeping techniques, introduction of good housekeeping incentives and encouragement of good housekeeping culture among the workforce.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.159
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.138
GPT teacher head0.499
Teacher spread0.362 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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