Good Housekeeping - A Panacea for Slips, Trips & Falls Accident in the NLNG Project, Bonny
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".