Edge One (United States)

Measuring public attitudes toward wildlife provides crucial insights into human relationships with nature and helps monitor progress toward Global Biodiversity Framework targets. Yet, conducting such assessments at a global scale presents challenges. Digital news and social media offer a rich record of public discourse, but extracting information about attitudes toward wildlife from these sources is not straightforward. Selecting effective search terms is complicated by differences between everyday names for taxa and their scientific or formal common names, and raw news and social media data are often cluttered with irrelevant content and syndicated articles. To address search term selection, we used a folk taxonomy approach that derives recognizable species groupings from shared common name endings. We identified syndicated articles by using cosine similarity on term frequency-inverse document frequency vectors. To filter out irrelevant content while minimizing the need for corpus-specific annotation and model training, we developed a 2-stage relevance filter that uses unsupervised learning to reveal common topics and an open-source zero-shot large language model (LLM) to assign topics to article titles and estimate relevance. We conducted sentiment, topic, and volume analyses on the resulting data. To illustrate our method, we examined news and X posts containing search terms for bats, pangolins, elephants, and gorillas from 2019 through 2021, a period that covers the onset of the COVID-19 pandemic. Up to 62% of articles containing bat search terms were unrelated to bats as wildlife, underscoring the importance of relevance filtering. News articles mentioning horseshoe bats, initially implicated in the outbreak, increased significantly in January 2020, with significant sentiment shifts in news and X posts mentioning horseshoe bats emerging later (October 2020). Our methods provide a practical application of modern, general-purpose natural language processing (NLP) tools, including LLMs, for analyzing public perceptions of biodiversity relative to current events or conservation outreach and marketing campaigns.

To compare individuals with mild and moderate/severe traumatic brain injury (TBI) on alcohol and drug use and substance use disorders before and in the first year post-TBI; to explore sociodemographic and injury-related variables associated with substance use disorders. A total of 225 adults hospitalized in a level I trauma center after TBI. Observational cohort study with retrospective (pre-TBI) and prospective (4, 8, and 12 months post-TBI) assessments. Mini International Neuropsychiatric Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). The percentage of participants using alcohol or drug declined shortly after the injury (4 months) but increased closer to preinjury levels by the end of the first year. Post-TBI alcohol use was higher after mild than moderate/severe TBI, but drug use was similar. About 11% of participants met criteria for a substance use disorder in the first year after TBI. Younger age, not being in a relationship, and suspected substance intoxication at the time of TBI were associated with the presence of a post-TBI substance use disorder. Individuals with milder injuries return to alcohol use earlier than those with more severe injuries. Given that substance use may alter recovery, preventive recommendations and systematic follow-ups are warranted regardless of injury severity and access to rehabilitation.

Stoughton, Mass. Editor's note: This article was based on the most current CDC information available at the time of publication. In October 2008, the CDC updated its advice, recommending treatment for persons diagnosed with active infestation and checking of household members and close contacts. Send comments by e-mail if possible to [email protected]. Please, no attachments. Or send them to Letters Editor, Nursing2009, 323 Norristown Rd., Suite 200, Ambler, PA 19002–2758. Please include your name, credentials, complete mailing address, e-mail address (if applicable), and daytime phone number. Letters are edited for content, length, and grammar.