Suchergebnisse

Purpose
Suicidal ideation (SI) is a serious public health concern among youth and young adults in the USA. It is critical to evaluate potential risk and protective factors associated with SI among young adults to develop prevention strategies that target novel mechanistic outcomes.


Design/methodology/approach
This study sought to evaluate the association between negative social exchange (NSE) and SI via the framework of the interpersonal theory of suicide. Participants were a diverse sample of N = 243 college students, with a mean age of 19.98 years (SD = 3.15). Participants were predominantly female (68.3%) and Hispanic/Latinx (39.5%), white (21.4%) or multiracial/multiethnic (24.3%). Participants completed survey measures to assess each of the constructs of interest.


Findings
Results indicated a significant indirect effect from NSE to SI via perceived burdensomeness, but not thwarted belongingness.


Originality/value
While individual-level interventions target emotional reactivity and managing emotional responses, larger public health–focused interventions on campus may wish to target reductions in NSE on campus. Future work should evaluate these associations using longitudinal study designs to further evaluate the statistical models.

Abstract: Background: Suicide prevention gatekeeper training programs offer a unique opportunity to increase knowledge about suicide prevention, enhance risk identification, and reduce suicide-related stigma. Aims: This study evaluated the efficacy of an online suicide prevention gatekeeper training program in a randomized trial. Method: Participants were US college students ( N = 388). Participants (69.3% women) had a mean age of 19.01 years and identified primarily as Hispanic/Latinx (52.1%) and non-Hispanic White (22.4%). Participants completed pre- and posttest surveys and were randomly assigned to either the ASK About Suicide to Save a Life (AS + K?) suicide prevention gatekeeper training program or an information-only comparison intervention. Results: Participants in the AS + K? condition reported significantly greater gatekeeper preparedness and self-efficacy, and lower stigmatized attitudes at posttraining, as compared with those in the comparison condition. There were no significant differences in likelihood of using gatekeeper skills or suicide-related knowledge across groups. Limitations: This short-term study was not able to assess behavioral change resulting in use of gatekeeper skills over time. Conclusions: Completion of gatekeeper training resulted in increases in preparedness and self-efficacy for engaging in gatekeeper behaviors, as well as a reduction in suicide-related stigma. Despite small intervention effects, the online AS + K? training appears to be a promising program.

Introduction: In 2017, the Royal College of Ophthalmologists UK published 'The Way Forward' describing the effects of the ageing UK population on clinical demand for macular conditions. Although one-stop clinics have become accepted standard practice for combined assessment and injections, there is little guidance regarding eventual discharge of patients, and practice varies between clinicians. In 2018, NHS Lothian started a multidisciplinary one-stop clinic involving an Ophthalmologist, a Medical Photographer, a specialist Low Vision Optometrist, and a Low Vision Counsellor. We aimed to detail our experiences of this novel multidisciplinary discharge clinic for advanced macular disease patients. We also aimed to assess patient-reported anxiety and depression outcomes following this clinic. Retrospective data on 60 patients who attended the clinic from August 2018 to January 2019 were collected and included in analysis. Average age at presentation to the clinic was 85.76 ± 8.18 years old and patients had been followed up in the macula clinic for a mean of 4.80 ± 2.43 years prior to attending the clinic. In all, 31 patients responded to a survey on anxiety and depression using the Hospital Anxiety and Depression score (HADS). Three (10%) of the patients reported scores abnormal for anxiety, and there were no abnormal scores for depression. The clinic provides a holistic approach for end-stage macular disease patients and reduces unnecessary macular anti–vascular endothelial growth factor treatments and clinic review appointments. This is especially important now during the coronavirus SARS-CoV-2 global pandemic. This provides significant benefits to capacity for delivery of clinical services and facilitates a safe and supported discharge for patients.

Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions.Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines.Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis.Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly.Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of ...

Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)–enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19–related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app–related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning–based approaches. Results: Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.

Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2.Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)–enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19–related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app–related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning–based approaches.Results: Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology.Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.