Technology-Based Interventions in Substance Use Treatment to Promote Health Equity Among People Who Identify as African American/Black, Hispanic/Latinx, and American Indian/Alaskan Native: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Technology-based interventions (TBIs; ie, web-based and mobile interventions) have the potential to promote health equity in substance use treatment (SUTx) for underrepresented groups (people who identify as African American/Black, Hispanic/Latinx, and American Indian/Alaskan Native) by removing barriers and increasing access to culturally relevant effective treatments. However, technologies (emergent and more long-standing) may have unintended consequences that could perpetuate health care disparities among people who identify as a member of one of the underrepresented groups. Health care research, and SUTx research specifically, is infrequently conducted with people who identify with these groups as the main focus. Therefore, an improved understanding of the literature at the intersection of SUTx, TBIs, and underrepresented groups is warranted to avoid exacerbating inequities and to promote health equity. OBJECTIVE: This study aims to explore peer-reviewed literature (January 2000-March 2021) that includes people who identify as a member of one of the underrepresented groups in SUTx research using TBIs. We further seek to explore whether this subset of research is race/ethnicity conscious (does the research consider members of underrepresented groups beyond their inclusion as study participants in the introduction, methods, results, or discussion). METHODS: Five electronic databases (MEDLINE, Scopus, Cochrane Library, CINAHL, and PsycInfo) were searched to identify SUTx research using TBIs, and studies were screened for eligibility at the title/abstract and full-text levels. Studies were included if their sample comprised of people who identify as a member of one of the underrepresented groups at 50% or more when combined. RESULTS: Title/abstract and full-text reviews were completed in 2021. These efforts netted a sample of 185 studies that appear to meet inclusionary criteria. Due to the uniqueness of tobacco relative to other substances in the SUTx space, as well as the large number of studies netted, we plan to separately publish a scoping review on tobacco-focused studies that meet all other criteria. Filtering for tobacco-focused studies (n=31) netted a final full-text sample for a main scoping review of 154 studies. The tobacco-focused scoping review manuscript is expected to be submitted for peer review in Spring 2022. The main scoping review data extraction and data validation to confirm the accuracy and consistency of data extraction across records was completed in March 2022. We expect to publish the main scoping review findings by the end of 2022. CONCLUSIONS: Research is needed to increase our understanding of the range and nature of TBIs being used in SUTx research studies with members of underrepresented groups. The planned scoping review will highlight research at this intersection to promote health equity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34508.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".