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Beyond Journals-Visual Abstracts Promote Wider Suicide Prevention Research Dissemination and Engagement: A Randomized Crossover Trial.

ABSTRACT: Background: Many academic institutions and journals disseminate research through social media to increase accessibility and reach a wider audience. "Visual Abstracts" are well-suited for social media dissemination, and have been adopted by some as a novel approach to increase engagement with academic content. Visual abstracts are a visual representation of key methods and findings from a traditional peer-reviewed publication. This study expands on previous research by examining the impact of visual abstracts compared to traditional text abstracts to disseminate research produced in a national research center focused on preventing Veteran suicide. Methods: A prospective, randomized crossover design was utilized to compare Twitter posts with a visual abstract to those with a simple screen grab of the PubMed abstract (n = 50 journal publications). Outcomes were measured using native Twitter Analytics to track impressions, retweets, total engagements, and link clicks about 28 days post-tweet, and Altmetric It to track additional alternative metric outcomes. Results: Visual abstract tweets were associated with a significantly higher number of impressions (p < 0.001), retweets (p < 0.001), and link clicks (p = 0.02) compared with text abstract tweets. Conclusions: In line with results from prior studies, we found that visual abstracts resulted in significantly greater research dissemination and social media engagement via retweets and link clicks compared with text tweets. These findings provide further evidence that visual abstracts increase awareness and readership of journal publications, and that Twitter is an effective platform for research dissemination beyond the traditional academic researcher audience. Implications highlight the importance of social media for suicide prevention advocates, Veteran health researchers and other stakeholders to communicate research findings.

SUBMITTER: Hoffberg AS

PROVIDER: S-EPMC8028397 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceSince the 1990s, reporting guidelines have developed that uniformly require authors to report a measure of precision (confidence intervals [CIs]) in addition to effect size.ObjectiveTo investigate the time trend of statistical inference and statistical reporting style in abstracts of major cancer journals.Design, setting, and participantsThis cross-sectional study reviewed all abstracts published between January 1, 1990, and December 31, 2020, in 10 high-ranking cancer journals (Lancet Oncology, Journal of Clinical Oncology, Cancer Discovery, Cancer Cell, JAMA Oncology, Annals of Oncology, Molecular Cancer, Journal of Thoracic Oncology, Journal of the National Cancer Institute, and Trends in Cancer) using a previously validated computerized algorithm to search the PubMed database. For the time trend analyses, 2 journals with only a few years of existence (JAMA Oncology and Trends in Cancer) were excluded.ExposuresCalendar year, journal, and type of abstract (randomized clinical trial or other).Main outcomes and measuresProportions of abstracts containing CIs, P values without CIs, and qualitative expressions of statistical significance only were compared over time among journals.ResultsOverall, 24 034 of 42 509 abstracts (56.5%) contained statistical inference. Reporting of CIs increased over time in 5 of 8 journals. From 2016 to 2020, the most prevailing statistical reporting style was the presentation of CIs (3070 of 4895 [62.7%]). However, the proportion of abstracts reporting statistical inference based solely on the terms significant or nonsignificant was still 1195 of 4895 (24.4%) during this period and was most prevalent among basic science-oriented cancer journals (eg, 63 of 66 [95.5%] in Cancer Cell). A higher prevalence of CI reporting was associated with reporting of results from randomized clinical trials and the requirement to report according to guidelines (eg, 522 of 574 [90.9%] in Lancet Oncology).Conclusions and relevanceThese findings suggest that the reporting style of statistical inference in abstracts of major cancer journals has improved over time. A requirement in journals' instructions for authors to present statistical inference in accordance with reporting guidelines and the implementation of these guidelines in submitted manuscripts on the part of journal editors may improve reporting.

Project description:ObjectivePatients with fibromyalgia (FM) are 10 times more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and suicide attempts in patients with FM and to identify interpretable risk and protective factors for suicidality unique to FM.MethodsThis was a case-control study of large-scale electronic health record data collected from 1998 to 2017, identifying FM cases with validated Phenotype KnowledgeBase criteria. Model performance was measured through discrimination, including the receiver operating area under the curve (AUC), sensitivity, and specificity, and through calibration, including calibration plots. Risk factors were selected by L1 penalized regression with bootstrapping for both outcomes. Secondary utilization analyses converted time-based billing codes to equivalent minutes to estimate face-to-face provider contact.ResultsWe identified 8,879 patients with FM, with 34 known suicide attempts and 96 documented cases of suicidal ideation. External validity was good for both suicidal ideation (AUC 0.80) and attempts (AUC 0.82) with excellent calibration. Risk factors specific to suicidal ideation included polysomatic symptoms such as fatigue (odds ratio [OR] 1.29 [95% confidence interval (95% CI) 1.25-1.32]), dizziness (OR 1.25 [95% CI 1.22-1.28]), and weakness (OR 1.17 [95% CI 1.15-1.19]). Risk factors specific to suicide attempt included obesity (OR 1.18 [95% CI 1.10-1.27]) and drug dependence (OR 1.15 [95% CI 1.12-1.18]). Per utilization analyses, those patients with FM and no suicidal ideation spent 3.5 times more time in follow-up annually, and those without documented suicide attempts spent more than 40 times more time face-to-face with providers annually.ConclusionThis is the first study to successfully apply machine learning to reliably detect suicidality in patients with FM, identifying novel risk factors for suicidality and highlighting outpatient engagement as a protective factor against suicide.

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Beyond Journals-Visual Abstracts Promote Wider Suicide Prevention Research Dissemination and Engagement: A Randomized Crossover Trial.

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