Examining stigma relating to substance use and contextual factors in social media discussions

The role of stigma in cannabis use disclosure: an exploratory study

BACKGROUND

Although cannabis use incidence, societal acceptance, and legislation all trend positively, cannabis remains federally illegal in the USA. Prior studies have revealed that patients are reluctant to disclose their cannabis use history in the healthcare system, which can negatively impact patient care. This study reports the frequency of cannabis use disclosure with special considerations for stigmatization. To better understand the limitations, providers face in providing collaborative, comprehensive, and informed care, this study evaluated four domains of stigma: perceived, anticipated, enacted, and internalized.

METHODS

This study used a descriptive exploratory design. Data collection occurred using an anonymous, online national survey with a convenience sample in the USA. Recruitment relied on electronic media and occurred between July and December 2022. Participants were adults older than 21 years and self-identified as having used cannabis and accessed the healthcare system within the last five years. The survey measured demographic characteristics, cannabis use, and disclosure patterns. Stigma was measured using the Stigma Use Stigma Mechanism Scale (SU-SMS) and Substance Abuse Use Self-Stigma Scale (SASSS) with language modifications for cannabis. Ordinal logistic regression models were performed to evaluate associations between the frequency of cannabis use disclosure patterns and each stigma category. Associations were assessed using Chi-squared or Fisher's exact tests.

RESULTS

Data were available for 249 respondents. Most participants (57.1%) reported initiating a conversation about cannabis with their healthcare provider; 27.8% of the time, cannabis is never discussed, and healthcare providers initiate only 15.1% of related discussions. Anticipated stigma [95% CI 1.045-1.164] and total stigma [95% CI 1.001-1.039] had statistically significant associations with nondisclosure. Annual household income (p = .04), chronicity of cannabis use (p = .03), frequency of cannabis use (p = .02), and a known amount of CBD in products consumed (p = .01) had statistically significant associations with the frequency of cannabis use disclosure.

CONCLUSIONS

Patients who use cannabis experience stigmatization in the healthcare setting that may limit disclosure of cannabis use history. Future studies would be well served to explore anticipated stigma more deeply. Healthcare providers should be knowledgeable to lead such conversations relating to cannabis while maintaining an unbiased perspective.

A linguistic analysis of dehumanization toward substance use across three decades of news articles

Introduction

Substances and the people who use them have been dehumanized for decades. As a result, lawmakers and healthcare providers have implemented policies that subjected millions to criminalization, incarceration, and inadequate resources to support health and wellbeing. While there have been recent shifts in public opinion on issues such as legalization, in the case of marijuana in the U.S., or addiction as a disease, dehumanization and stigma are still leading barriers for individuals seeking treatment. Integral to the narrative of "substance users" as thoughtless zombies or violent criminals is their portrayal in popular media, such as films and news.

Methods

This study attempts to quantify the dehumanization of people who use substances (PWUS) across time using a large corpus of over 3 million news articles. We apply a computational linguistic framework for measuring dehumanization across three decades of New York Times articles.

Results

We show that (1) levels of dehumanization remain high and (2) while marijuana has become less dehumanized over time, attitudes toward other substances such as heroin and cocaine remain stable.

Discussion

This work highlights the importance of a holistic view of substance use that places all substances within the context of addiction as a disease, prioritizes the humanization of PWUS, and centers around harm reduction.

Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature

Introduction

Medications such as buprenorphine and methadone are effective for treating opioid use disorder (OUD), but many patients face barriers related to treatment and access. We analyzed two sources of data-social media and published literature-to categorize and quantify such barriers.

Methods

In this mixed methods study, we analyzed social media (Reddit) posts from three OUD-related forums (subreddits): r/suboxone, r/Methadone, and r/naltrexone. We applied natural language processing to identify posts relevant to treatment barriers, categorized them into insurance- and non-insurance-related, and manually subcategorized them into fine-grained topics. For comparison, we used substance use-, OUD- and barrier-related keywords to identify relevant articles from PubMed published between 2006 and 2022. We searched publications for language expressing fear of barriers, and hesitation or disinterest in medication treatment because of barriers, paying particular attention to the affected population groups described.

Results

On social media, the top three insurance-related barriers included having no insurance (22.5%), insurance not covering OUD treatment (24.7%), and general difficulties of using insurance for OUD treatment (38.2%); while the top two non-insurance-related barriers included stigma (47.6%), and financial difficulties (26.2%). For published literature, stigma was the most prominently reported barrier, occurring in 78.9% of the publications reviewed, followed by financial and/or logistical issues to receiving medication treatment (73.7%), gender-specific barriers (36.8%), and fear (31.5%).

Conclusion

The stigma associated with OUD and/or seeking treatment and insurance/cost are the two most common types of barriers reported in the two sources combined. Harm reduction efforts addressing barriers to recovery may benefit from leveraging multiple data sources.