Jon Agley’s research while affiliated with Indiana University Bloomington and other places

Introduction: Perceived social support is a psychological construct that is used to describe the 'perception of adequacy' of the support being provided by a person's social network. Higher perceived social support has been linked to multiple benefits across numerous studies over the past several decades and among multiple populations. The Multidimensional Scale of Perceived Social Support (MSPSS) is a 12-item scale to assess the construct of perceived social support. The instrument has been translated to approximately 35 languages and dialects, but it has rarely been translated into tribal languages, which may be commonly spoken in rural areas. Further, such translations have not always been accompanied by cultural adaptation. Assessment of the encultured meaning of terms from a validated instrument is important alongside translation because words and terms related to perceived social support can be culturally specific. As such, this article presents a community-engaged research approach to develop a translation of the MSPSS into Diné bizaad (Navajo), along with a qualitative assessment of the meaning and implications of key terminology from the instrument. Methods: This study was led by a faculty member at a Native American-Serving Nontribal Institution (NASNTI) in south-western Colorado, US. Additional research collaborators included the original developer of the English MSPSS, a researcher with experience in methodology, and a member of the local Navajo community who was a student at the NASNTI. Using convenience and snowball sampling, a 2.5-hour focus group discussion was conducted and audio-recorded in May 2023 with eight Navajo community members who met eligibility criteria (including fluency in both Diné and English). All participants provided consent and received gratitude gifts for completion. Participants were four males and four females with an age range of 30-60 years. Since the discussion and recording were bilingual, a written English and Diné transcription was produced and anonymized, then reviewed by researchers. It was then backward-forward translated to English, then checked with discussion participants to validate accuracy. Using the general inductive method, key concepts and codes were separately identified and documented using NVivo 21 by two researchers. Full consensus as to coding was achieved over a sequence of six iterative consensus meetings among the coders. Results: The project was able to produce a harmonized version of the MSPSS translated into Diné bizaad that accounted for variation in meaning and intent of multiple core concepts of perceived social support. For example, concepts of 'family' and 'friend' were often characterized by ethnic clan-based close-knit bonds. The concept of 'significant other' often meant 'spouse' but sometimes also meant someone who was highly trusted, especially among unmarried discussants. 'Social support' was trust-based rather than need-based and often relied on gender-concordant bonding. Many additional nuances were discussed and are outlined in the full study. Discussion: Navajo community members were excited to collaboratively work on making this screening tool available in Diné bizaad. The translation that emerged from this process likely differed from what would have emerged from a direct translation without community input. Next steps for the tool should include quantitative reliability and validity analyses.

Background: Despite widespread naloxone distribution efforts, opioid-involved overdose rates remain high, with rates in recent years indicating disproportionate increases in the death rate for Black residents. This study evaluated knowledge and perceived competency regarding opioid overdose and naloxone administration among urban Indiana residents. Methods: As part of a federally funded project (#CPIMP221346), the Indiana University Center for Survey Research conducted community probability surveys from March to May 2023, sampling 772 residents in Indiana's urban communities across 8 zip code areas. The study explored individual and community level factors affecting residents' knowledge and perceived competency regarding opioid overdose and naloxone administration. Results: Multilevel analysis identified race, sex, household income, education, length of time lived in community, and a history of opioid overdose as significant predictors of knowledge. Participants who identified as White scored a mean of 6.65 out of 10, those of Other races scored 5.75, and Black participants scored 5.70, on a 10 item questionnaire developed from the Overdose Knowledge Scale (OOKS) and the Opioid Overdose Attitudes Scale (OOAS) (p < 0.001). Additionally, there was significant cross-level interaction between race and poverty, such that Black residents living in poorer neighborhoods displayed lower knowledge scores than White counterparts (ꞵ = 1.06, p = 0.039). However, in terms of perceived competency, only age and a history of opioid overdose, were found to be significant predictors. Conclusions: These findings underscore the importance of community education to increase knowledge and perceived competency regarding opioid overdose and naloxone administration among Black populations particularly living in socioeconomically disadvantated areas.

Background Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing “street names” of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, “brand name” references were more amenable to machine-driven categorization. Objective This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. Methods We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet’s engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency–inverse document frequency score. Results Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. Conclusions BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non–drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

Online participant recruitment (“crowdsourcing”) platforms are increasingly being used for research studies. While such platforms can rapidly provide access to large samples, there are concomitant concerns around data quality. Researchers have studied and demonstrated means to reduce the prevalence of low‐quality data from crowdsourcing platforms, but approaches to doing so often involve rejecting work and/or denying payment to participants, which can pose ethical dilemmas. We write this essay as an associate professor and two institutional review board (IRB) directors to provide a perspective on the competing interests of participants/workers and researchers and to propose a checklist of steps that we believe may support workers' agency on the platform and lessen instances of unfair consequences to them while enabling researchers to definitively reject lower‐quality work that might otherwise reduce the likelihood of their studies producing true results. We encourage further, explicit discussion of these issues among academics and among IRBs.

In healthcare and related fields, there is often a gap between research and practice. Scholars have developed frameworks to support dissemination and implementation of best practices, such as the Interactive Systems Framework for Dissemination and Implementation, which shows how scientific innovations are conveyed to practitioners through tools, training, and technical assistance (TA). Underpinning those aspects of the model are evaluation and continuous quality improvement (CQI). However, a recent meta-analysis suggests that the approaches to and outcomes from CQI in healthcare vary considerably, and that more evaluative work is needed. Therefore, this paper describes an assessment of CQI processes within the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Technology Transfer Center (TTC) Network, a large TA/TTC system in the United States comprised of 39 distinct centers. We conducted key informant interviews ( n = 71 representing 28 centers in the Network) and three surveys (100% center response rates) focused on CQI, time/effort allocation, and Government Performance and Results Act (GPRA) measures. We used data from each of these study components to provide a robust picture of CQI within a TA/TTC system, identifying Network-specific concepts, concerns about conflation of the GPRA data with CQI, and principles that might be studied more generally.

It is important to use evidence-based programs and practices (EBPs) to address major public health issues. However, those who use EBPs in real-world settings often require support in bridging the research-to-practice gap. In the US, one of the largest systems that provides such support is the Substance Abuse and Mental Health Services Administration’s (SAMHSA’s) Technology Transfer Center (TTC) Network. As part of a large external evaluation of the Network, this study examined how TTCs determine which EBPs to promote and how to promote them. Using semi-structured interviews and pre-testing, we developed a “Determinants of Technology Transfer” survey that was completed by 100% of TTCs in the Network. Because the study period overlapped with the onset of the COVID-19 pandemic, we also conducted a retrospective pre/post-pandemic comparison of determinants. TTCs reported relying on a broad group of factors when selecting EBPs to disseminate and the methods to do so. Stakeholder and target audience input and needs were consistently the most important determinant (both before and during COVID-19), while some other determinants fluctuated around the pandemic (e.g., public health mandates, instructions in the funding opportunity announcements). We discuss implications of the findings for technology transfer and frame the analyses in terms of the Interactive Systems Framework for Dissemination and Implementation.

Retrospective alcohol use data are prone to recall bias, a limitation that could be addressed with real-time ecological momentary assessment (EMA) tools. We aimed to (1) introduce a simple (single-click) EMA methodology for collecting real-time alcohol use data, and (2) investigate the EMA methodology’s performance relative to established alcohol use data collection tools. In March–April 2021, we sampled undergraduate students (n = 84) and collected a week of alcohol use data. Participants entered their real-time drinking start times using our EMA methodology, self-reported their drinking details in daily surveys, and a subsample recorded their breath alcohol concentration (BrAC) using smart breathalyzers. We estimated the accuracy of our EMA methodology in collecting alcohol use data relative to data collected by daily surveys and breathalyzers. Overall, 199 drinking events were recorded with the EMA methodology. Numbers of drinks recorded with the EMA methodology were correlated with self-reported daily surveys (r = .82, p < .001) and BrAC readings (r = .69, p < .001). Sensitivity and specificity of the EMA methodology in detecting heavy drinking relative to daily surveys were 82% (95% CI [67%, 92%]) and 97% (95% CI [85%, 100%]), respectively. These were 74% (95% CI: [64%, 83%]) and 92% (95% CI: [85%, 96%]) for binge drinking. Similar results were found when we used breathalyzers as the reference standard test. We developed an EMA methodology for collecting real-time alcohol use data (alcohol drinking start-time, frequency, magnitude, patterns, and pace). Our findings support the utility of our EMA methodology in collecting alcohol use data among college students.