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Record W4414024285 · doi:10.2196/67401

The Development of a Patient-Centered Digital Health Care Technology for Young Adults in Opioid Use Disorder Treatment: Qualitative Study

2025· article· en· W4414024285 on OpenAlexvenueno aff
Karen Alexander, Madison Scialanca

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintQualitative researchOpioid use disorderHealth careOpioidMedicinePsychologyComputer scienceWorld Wide WebSociologyPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Background: Young adults, defined as individuals between the ages of 18 and 29 years, drop out of opioid use disorder (OUD) treatment more often than older adults. Premature treatment drop-out substantially increases fatal overdose risk. Self-monitoring through text messaging has been researched extensively among people with OUD to identify drop-out risk factors. Self-monitoring could potentially improve methadone treatment engagement among young adults, who are a population that is both hard to reach and more likely to use technology compared to older adults. Self-monitoring can increase risk factor awareness and help patients and counselors develop targeted coping strategies and treatment plans. However, embedding a discussion of risk factor information into existing counseling sessions has been limited and may offer a promising opportunity to improve engagement among young adults. Objective: This pilot proof-of-concept study examined the implementation of self-monitoring intervention, AWARE (Awareness and Response to the Environment), designed to bring attention to treatment drop-out risk factors among young adults and create discussion about risk factors with their existing treatment counselor. Methods: In this formative research, a convenience sample (N=8) of young adults (n=3, 38%) in methadone treatment, their counselors (n=3, 38%), and clinic leadership (n=2, 25%) were recruited from an opioid treatment program after referral from treatment staff. Participants were interviewed to obtain feedback as AWARE was developed. In semistructured interviews, perspectives regarding barriers to treatment for young adults and AWARE utility were obtained. Concurrently, 3 dyads of young adults (n=3, 38%) and counselors (n=3, 38%) piloted the intervention daily for 4 weeks. Results: The 3 consented young adults with OUD participants (n=2, 67% female; n=2, 67% Latino/a) were sent daily surveys for 28 days (53% overall completion rate). Young adults and counselors found AWARE relevant to their treatment experience and acceptable to complete over 4 weeks. The most reported daily stressors included concerns about the health and well-being of a family member, challenges with staying organized, and feeling overwhelmed by responsibilities without adequate support. In qualitative interviews, counselors and clinic leadership reported that AWARE presented a relevant, new way to engage young adults daily, in addition to weekly counseling sessions. Young adults felt that prompts sent by AWARE offered a type of social support they lacked, like "someone checking in on them." Conclusions: Overall, young adult and counselor participants were able to engage in AWARE in a busy clinic environment, and participants and clinic leadership found it valuable. By addressing common stressors and providing a sense of social connection, AWARE may help fill a gap in support between counseling sessions. However, the study was limited by the small number of young adults engaging in methadone treatment. Further research is needed to refine the measures and methods of AWARE and evaluate its effectiveness.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.041
GPT teacher head0.442
Teacher spread0.401 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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