Literature DB >> 15898090

Classification and coding of commercial fishing injuries by work processes: an experience in the Danish fresh market fishing industry.

O C Jensen1, S Stage, P Noer.   

Abstract

BACKGROUND: Work-related injuries in commercial fishing are of concern internationally. To better identify the causes of injury, this study coded occupational injuries by working processes in commercial fishing for fresh market fish.
METHODS: A classification system of the work processes was developed by participation in fishing vessel trips where observations and video recordings of the work operations on board were collected. Subsequently the system was pilot tested using the Danish Maritime Authority injury reports.
RESULTS: The developed classification system contains 17 main categories and up to 13 sub-categories of the work processes for each of the five different types of fishing. A total of 620 injury reports were reviewed and coded. Five percent (n = 33) of these were fatal injuries. The working processes were identified and coded according to the developed classification system for 553 (89%) injury reports: Danish seiner (n = 83), gill-netter (n = 122), beam trawler (n = 71), twin-trawler 2-T (n = 96), single/pair trawler 1-T (n = 181). Sixty-seven (11%) of the reports were unclassifiable due to lack of information. Preparing, shooting, and hauling of the gear and nets accounted for 50% of the injuries; they were most serious type of injuries such as fractures and sprains. Walking about the ship, in particular embarking and disembarking, climbing and descending ladders accounted for nearly one-fifth of the injuries.
CONCLUSION: We found that the working processes related to working with the gear and nets vary greatly in the different fishing methods. Coding of the injuries to the specific working processes allows for targeted prevention efforts.

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Year:  2005        PMID: 15898090     DOI: 10.1002/ajim.20163

Source DB:  PubMed          Journal:  Am J Ind Med        ISSN: 0271-3586            Impact factor:   2.214


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