影响工作人员支持自愿无铅弹药外展方案的因素

Q2 Social Sciences
John H. Schulz, Sonja A. Wilhelm Stanis, C. Li, M. Morgan, Elisabeth B. Webb
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引用次数: 2

摘要

白头海雕因误食子弹碎片和未取回的鹿尸体而导致铅中毒,继续导致死亡。为了解决这一问题,美国鱼类和野生动物管理局(USFWS)中西部地区在2016-2018年期间启动了一项外展计划,鼓励猎人在国家野生动物保护区(NWRs)猎鹿时自愿使用无铅弹药。我们进行了一项调查,使用可能影响USFWS员工对无铅外展计划支持的七个因素来评估该计划:创新性,白头鹰接触铅,培训和外展材料的可用性,对计划实施的满意度,信息材料的重要性,以及他们是否是猎鹿人。多元线性回归表明,对鹰的领先风险和信息材料的重要性的态度对项目支持的影响最大,其次是个人的创新能力。虽然以前的研究集中在影响猎人的无铅使用上,但我们的研究结果表明,工作人员对无铅的态度和看法强烈地影响了项目的支持和最终的项目结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Factors affecting staff support of a voluntary nonlead ammunition outreach program
Abstract Lead poisoning from ingestion of bullet fragments in gut piles and unretrieved deer carcasses continues to cause mortality in bald eagles. To address this issue, the Midwest region of the U.S. Fish and Wildlife Service (USFWS) initiated an outreach program during 2016–2018 encouraging hunters to voluntarily use nonlead ammunition while deer hunting on National Wildlife Refuges (NWRs). We conducted a survey to assess this program using seven factors that might influence USFWS staff support for the nonlead outreach program: innovativeness, lead exposure to bald eagles, availability of training and outreach materials, satisfaction with program implementation, importance of informational materials, and if they were a deer hunter. Multiple linear regression showed attitudes toward eagles’ lead risk and importance of informational materials had the strongest effect on program support, followed by an individual’s innovativeness. Although previous studies have focused on influencing hunters’ nonlead use, our findings show staff attitudes and perceptions about nonlead strongly affect program support and ultimately program outcomes.
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
15
期刊介绍: Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.
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