@article{ref1,
title="Freely available training videos for suicide prevention: scoping review",
journal="JMIR mental health",
year="2023",
author="Wislocki, Katherine and Jager-Hyman, Shari and Brady, Megan and Weiss, Michal and Schaechter, Temma and Khazanov, Gabriela and Young, Sophia and Becker-Haimes, Emily",
volume="10",
number="",
pages="e48404-e48404",
abstract="BACKGROUND: Freely available and asynchronous implementation supports can reduce the resource burden of evidence-based practice training to facilitate uptake. Freely available web-based training videos have proliferated, yet there have been no efforts to quantify their breadth, depth, and content for suicide prevention. <br><br>OBJECTIVE: This study presents results from a scoping review of freely available training videos for suicide prevention and describes a methodological framework for reviewing such videos. <br><br>METHODS: A scoping review of freely available training videos (≥2 minutes) for suicide prevention practices was conducted using 4 large video-sharing platforms: YouTube, Vimeo, Bing Video, and Google Video. Identified suicide prevention training videos (N=506) were reviewed and coded. <br><br>RESULTS: Most content was targeted toward gatekeepers or other lay providers (n=370) versus clinical providers (n=136). Videos most commonly provided content related to suicidal thoughts or behaviors (n=420). Many videos (n=274, 54.2%) included content designed for certain communities or organizations. Less than half (n=232, 45.8%) of training videos included formal clinical content pertaining to assessment or intervention for suicide prevention. <br><br>CONCLUSIONS: Results suggested an abundance of videos providing broad informational content (eg, &quot;signs and symptoms of someone at risk for suicide&quot;) and a limited portion of videos with instructional content aimed at clinical providers delivering formal evidence-based assessments or interventions for suicide prevention. Development of resources to address identified gaps may be needed. Future work may leverage machine learning techniques to expedite the review process.<p /> <p>Language: en</p>",
language="en",
issn="2368-7959",
doi="10.2196/48404",
url="http://dx.doi.org/10.2196/48404"
}