Women Empowered to Connect With Addiction Resources and Engage in Evidence-Based Treatment (WE-CARE)—an mHealth Application for the Universal Screening of Alcohol, Substance Use, Depression, and Anxiety: Usability and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Women of childbearing age (aged 18-44 years) face multiple barriers to receiving screening and treatment for unhealthy alcohol and substance use, depression, and anxiety, including lack of screening in the primary care setting and lack of support in accessing care. The Women Empowered to Connect with Addiction Resources and Engage in Evidence-based Treatment (WE-CARE) mobile app was developed to test universal screening with women of childbearing age and linkage to care after an anonymous assessment. OBJECTIVE: In this study, we aimed to investigate the feasibility and acceptability of providing anonymous screening instruments through mobile phones for alcohol and substance use, as well as depression and anxiety, for women of childbearing age. METHODS: We used agile development principles based on previous formative research to test WE-CARE mobile health app with women of childbearing age (N=30) who resided in 1 of 6 counties in central Florida. WE-CARE included screening instruments (for alcohol, substance use, depression, and anxiety), a moderated discussion forum, educational microlearning videos, a frequently asked questions section, and resources for linkage to treatment. Individuals were recruited using flyers, academic listserves, and a commercial human subject recruiting company (Prolific). Upon completion of the screening instruments, women explored the educational and linkage to care features of the app and filled out a System Usability Scale to evaluate the mobile health app's usability and acceptability. Postpilot semistructured interviews (n=4) were conducted to further explore the women's reactions to the app. RESULTS: A total of 77 women downloaded the application and 30 completed testing. Women of childbearing age gave the WE-CARE app an excellent System Usability Scale score of 86.7 (SD 12.43). Our results indicate elevated risk for substance use in 18 of the 30 (60%) participants, 9/18 (50%) also had an elevated risk for anxiety or depression, and 11/18 (61%) had an elevated risk for substance use, anxiety, or depression. Participants reported that WE-CARE was easy to navigate and use but they would have liked to see more screening questions and more educational content. Linkage to care was an issue; however, as none of the women identified as "at-risk" for substance use disorders contacted the free treatment clinic for further evaluation. CONCLUSIONS: The mobile health app was highly rated for acceptability and usability, but participants were not receptive to seeking help at a treatment center after only a few brief encounters with the app. The linkage to care design features was likely insufficient to encourage them to seek treatment. The next version of WE-CARE will include normative scores for participants to self-evaluate their screening status compared with their age- and gender-matched peers and enhanced linkages to care features. Future development will focus on enhancing engagement to improve change behaviors and assess readiness for change.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".