Agricultural and Biological Engineering, University of Florida, 1741 Museum Road, PO Box 110570, Gainesville, FL 32611, USA.
National Farm Medicine Center, Marshfield Clinic Research Institute 1000 N. Oak Ave., Marshfield, WI 54449, USA.
J Safety Res. 2020 Dec;75:111-118. doi: 10.1016/j.jsr.2020.08.006. Epub 2020 Sep 10.
To determine coders' agreement level for the Occupational Injury and Illness Classification System (OIICS) source of injury and injury event codes, and the Farm and Agricultural Injury Classification (FAIC) code in the AgInjuryNews.org and to determine the effects of supplemental information and follow-up discussion in final code assignments.
Two independent researchers initially coded 1304 injury cases from AgInjurynews.org using the OIICS and the FAIC coding schemes. Code agreement levels for injury source, event, and FAIC and the effect of supplemental information and follow-up discussions on final coding was assessed.
Coders' agreement levels were almost perfect for OIICS source and event categories at the 3-digit level, with lower agreement at the 4-digit level. By using supplemental information and follow-up discussion, coders improved the coding accuracy by an average 20% for FAIC. Supplemental information and follow-up discussions had helped finalize the disagreed codes 55% of the time for OIICS source coding assignments and 40% of time for OIICS event coding assignments for most detailed 4-digit levels. Five key themes emerged regarding accurate and consistent coding of the agricultural injuries: inclusion/exclusion based on industry classification system; inconsistent/discrepant reports; incomplete/nonspecific reports; effects of supplemental information on coding; and differing interpretations of code selection rules. Practical applications: Quantifying the level of agreement for agricultural injuries will lead to a better understanding of coding discrepancies and may uncover areas for improvement to coding scheme itself. High level of initial and final agreement with FAIC and OIICS codes suggest that these coding schemes are user-friendly and amenable to widespread use.
确定职业伤害和疾病分类系统（OIICS）伤害源和伤害事件代码以及农业伤害新闻网（AgInjuryNews.org）中的农业伤害分类（FAIC）代码的编码员之间的一致性水平，并确定补充信息和最终编码后讨论对代码分配的影响。
两名独立研究人员最初使用 OIICS 和 FAIC 编码方案对来自 AgInjurynews.org 的 1304 例伤害病例进行编码。评估了伤害源、事件和 FAIC 的代码一致性水平，以及补充信息和后续讨论对最终编码的影响。
OIICS 伤害源和事件类别的编码员一致性水平在 3 位数级别几乎是完美的，而在 4 位数级别则较低。通过使用补充信息和后续讨论，编码员将 FAIC 的编码准确率提高了平均 20%。补充信息和后续讨论有助于最终确定 OIICS 伤害源编码分配中 55%的不一致代码，而在 OIICS 事件编码分配中则有 40%的不一致代码。在对农业伤害进行准确和一致编码方面，出现了五个关键主题：基于行业分类系统的包括/排除；不一致/有差异的报告；不完整/不具体的报告；补充信息对编码的影响；以及对代码选择规则的不同解释。
量化农业伤害的一致性水平将有助于更好地理解编码差异，并可能发现编码方案本身需要改进的领域。FAIC 和 OIICS 代码的初始和最终高度一致性表明，这些编码方案易于使用，并且可以广泛使用。
