Status of ISO 45001;2018 Implementation in Seaports; A Case Study
Bibliographic record
Abstract
Seaports are global players in the maritime industry with the responsibility of promoting the well-being of employees and customers in the workplace.However, over the past years safety performance of seaports operating within the West African sub-region has become a major concern due to the lack of enforcement of national occupational health and safety regulations.This has compelled some seaports to adopt the occupational health and safety management system (OHSMS) ISO 45001:2018 standard.The aim of this study is therefore to assess the progress seaports have made in the implementation of ISO 45001:2018 standard and discuss the challenges that need to be overcome when implementing ISO 45001:2018 standard in seaports.Through the use of questionnaire, observations and review of institutional document, data was gathered from workers and senior managers of export, container depot and the stevedoring sections of seaports operating in Ghana.The findings of this study revealed that seaports within Ghana have made some progress with regards to workers' awareness of occupational health and safety issues, usage of personal protective equipment (PPEs) and safe working procedures.However, it was also established that a lot more needs to be done to improve the current levels of communication, safety training and resources in relation to health and safety management.The findings of this research are useful to maritime institutions wishing to migrate from OHSAS 18001:2007 certification to ISO 45001:2018 certification or seeking to improve on already existing system.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".