Buprenorphine Initiation in the Emergency Department: a Thematic Content Analysis of a #firesidetox Tweetchat

Chenworth, Megan; Perrone, Jeanmarie; Love, Jennifer S.; Greller, Howard A.; Sarker, Abeed; Chai, Peter R.

doi:10.1007/s13181-019-00754-7

Cited by 10 publications

(13 citation statements)

References 6 publications

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“…Our search yielded 9308 studies published between January 1, 1960, and October 5, 2021, with 1280 remaining after removal of duplicates and 552 assessed for eligibility (eFigure 1 in Supplement 1). Of 283 studies…”

Section: Resultsmentioning

confidence: 99%

“…The number of studies increased over time. For example, 4 studieswere published in 2000 and 21 in 2021, with a high of 31 in 2020 (eTable 2, eTable 3, eTable 4, eTable 5, and eFigure 2 in Supplement 1). Together, the included studies describe the views of 66 732 physicians who largely practiced general practice, internal medicine, or family medicine primarily in an office setting in the US.…”

Section: Resultsmentioning

confidence: 99%

“…Some studies addressed more than 1 intervention. Alcohol (86 studies), nicotine (30 studies), and opioids (104 studies) were most often studied alone. Among studies reporting on multiple drugs (44 studies), alcohol was included most often (38 studies…”

Section: Resultsmentioning

confidence: 99%

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Physician Reluctance to Intervene in Addiction

Campopiano von Klimo,

Nolan,

Corbin

et al. 2024

JAMA Netw Open

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ImportanceThe overdose epidemic continues in the US, with 107 941 overdose deaths in 2022 and countless lives affected by the addiction crisis. Although widespread efforts to train and support physicians to implement medications and other evidence-based substance use disorder interventions have been ongoing, adoption of these evidence-based practices (EBPs) by physicians remains low.ObjectiveTo describe physician-reported reasons for reluctance to address substance use and addiction in their clinical practices using screening, treatment, harm reduction, or recovery support interventions.Data SourcesA literature search of PubMed, Embase, Scopus, medRxiv, and SSRN Medical Research Network was conducted and returned articles published from January 1, 1960, through October 5, 2021.Study SelectionPublications that included physicians, discussed substance use interventions, and presented data on reasons for reluctance to intervene in addiction were included.Data Extraction and SynthesisTwo reviewers (L.N., M.C., L.F., J.P., C.S., and S.W.) independently reviewed each publication; a third reviewer resolved discordant votes (M.C. and W.C.). This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and the theoretical domains framework was used to systematically extract reluctance reasons.Main Outcomes and MeasuresThe primary outcome was reasons for physician reluctance to address substance use disorder. The association of reasons for reluctance with practice setting and drug type was also measured. Reasons and other variables were determined according to predefined criteria.ResultsA total of 183 of 9308 returned studies reporting data collected from 66 732 physicians were included. Most studies reported survey data. Alcohol, nicotine, and opioids were the most often studied substances; screening and treatment were the most often studied interventions. The most common reluctance reasons were lack of institutional support (173 of 213 articles [81.2%]), knowledge (174 of 242 articles [71.9%]), skill (170 of 230 articles [73.9%]), and cognitive capacity (136 of 185 articles [73.5%]). Reimbursement concerns were also noted. Bivariate analysis revealed associations between these reasons and physician specialty, intervention type, and drug.Conclusions and RelevanceIn this systematic review of reasons for physician reluctance to intervene in addiction, the most common reasons were lack of institutional support, knowledge, skill, and cognitive capacity. Targeting these reasons with education and training, policy development, and program implementation may improve adoption by physicians of EBPs for substance use and addiction care. Future studies of physician-reported reasons for reluctance to adopt EBPs may be improved through use of a theoretical framework and improved adherence to and reporting of survey development best practices; development of a validated survey instrument may further improve study results.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Physician Reluctance to Intervene in Addiction

Campopiano von Klimo,

Nolan,

Corbin

et al. 2024

JAMA Netw Open

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“…Manual annotation is relatively accurate, and by manually coding the data researchers can gain a deeper understanding and identify additional themes. For example, small‐scale studies of social media posts have provided important insights, such as the informal coping strategies used by people who use opioids during the COVID‐19 pandemic [18] or medical perspectives on buprenorphine treatment in emergency department settings [19]. However, it is a relatively burdensome method, and when confronted with an extremely large data set, researchers must either (a) spend a significant amount of time manually annotating all the available data or (b) limit annotation to a small subset of the available data.…”

Section: Media‐related Text Data: What Is It and How Have Substance U...mentioning

confidence: 99%

How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data

Riordan,

Bonela,

et al. 2024

Addiction

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A vast amount of media‐related text data is generated daily in the form of social media posts, news stories or academic articles. These text data provide opportunities for researchers to analyse and understand how substance‐related issues are being discussed. The main methods to analyse large text data (content analyses or specifically trained deep‐learning models) require substantial manual annotation and resources. A machine‐learning approach called ‘zero‐shot learning’ may be quicker, more flexible and require fewer resources. Zero‐shot learning uses models trained on large, unlabelled (or weakly labelled) data sets to classify previously unseen data into categories on which the model has not been specifically trained. This means that a pre‐existing zero‐shot learning model can be used to analyse media‐related text data without the need for task‐specific annotation or model training. This approach may be particularly important for analysing data that is time critical. This article describes the relatively new concept of zero‐shot learning and how it can be applied to text data in substance use research, including a brief practical tutorial.

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“…They provide an opportunity to quickly access the opinions of large groups of people around the world, an appropriate method of outreach given the social distancing and travel restrictions in place due to COVID-19. Content mining of tweets has previously been shown to aid in addressing challenges in various areas of healthcare and research, such as health promotion [ 4 ], breast cancer survivors [ 5 ], and buprenorphine initiation in the emergency department [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the #CovidPain Tweet Chat During the First Wave of the COVID-19 Pandemic in 2020

Furlan¹,

Deldar²,

Berezin³

et al. 2021

Cureus

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Introduction: In March 2020, we organized two tweet chats to discuss the COVID-19 pandemic and its impact on people affected by chronic pain. The objective of this study is to evaluate the #CovidPain tweet chat activities that took place at the early stages of the COVID-19 pandemic.Methods: We performed a quantitative analysis of the magnitude, range, engagement, and sentiment of each tweet chat. The data was extracted from Twitter and analyzed in Twitter Analytics and Symplur Signals using frequency and distributions. Then, we conducted a qualitative content analysis of the narrative tweets generated in response to the questions posted during the tweet chats.Results: The two tweet chats attracted 2305 participants, which generated 4351 tweets. The participants were healthcare providers, patient advocates, researchers/academics, and caregivers. COVID-19 had both negative and positive impacts. The negative consequences of COVID-19 included the reduction of physical activity, canceled appointments and treatments, more isolation, deterioration of preexisting mental health problems, and economic consequences. The positive consequences included efficient use of telemedicine, innovative methods for self-management, and at-home interventions.Conclusion: Twitter and tweet chats are useful in involving a diverse group of stakeholders for taking a deep dive into the topical issues relevant to a community that might be disproportionately affected by a public health crisis.

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Buprenorphine Initiation in the Emergency Department: a Thematic Content Analysis of a #firesidetox Tweetchat

Cited by 10 publications

References 6 publications

Physician Reluctance to Intervene in Addiction

Physician Reluctance to Intervene in Addiction

How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data

An Analysis of the #CovidPain Tweet Chat During the First Wave of the COVID-19 Pandemic in 2020

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