Using Social Media to Engage Justice-Involved Young Adults in Digital Health Interventions for Substance Use: Pilot Feasibility Survey Study
Notice bibliographique
Résumé
BACKGROUND: Young adults involved in the justice system have high rates of substance use disorders and low rates of treatment engagement. Most justice-involved young adults are supervised in the community-not incarcerated in jail or prison-where they have ongoing access to substances and experience significant barriers to care. When they do engage in treatment, they tend to have worse outcomes than justice-involved adolescents and older adults. Despite the need to develop targeted treatments, there are unique challenges in recruiting this population into clinical research. Digital health technology offers many novel avenues for recruiting justice-involved young adults into clinical research studies and disseminating substance use disorder treatments to justice-involved young adults. Because the vast majority of young adults regularly use one or more social media platforms, social media may offer a cost-effective and efficient way to achieve these goals. OBJECTIVE: This study aimed to describe the process and feasibility of using social media platforms (Facebook and Reddit) to recruit justice-involved young adults into clinical research. Justice-involved young adults recruited from these platforms completed a survey assessing the acceptability of digital health interventions to address substance use in this population. METHODS: Justice-involved young adults (aged 18-24 years) were recruited through paid advertisements placed on Facebook and Reddit. Participants responded to a web-based survey focused on their substance use, treatment use history, and acceptability of various digital health interventions focused on substance use. RESULTS: A national sample of justice-involved young adults were successfully enrolled and completed the survey (N=131). Participants were racially diverse (8/131, 6.1% American Indian individuals; 27/131, 20.6% Asian individuals; 23/131, 17.6% Black individuals; 26/131, 19.8% Latinx individuals; 8/131, 6.1% Pacific Islander individuals; 49/131, 37.4% White individuals; and 2/131, 1.5% individuals who identified as "other" race and ethnicity). Advertisements were cost-effective (US $0.66 per click on Facebook and US $0.47 per click on Reddit). More than half (72/131, 54.9%) of the participants were on probation or parole in the past year and reported hazardous alcohol (54/131, 51.9%) or drug (66/131, 57.4%) use. Most of the participants (103/131, 78.6%) were not currently participating in substance use treatment. Nearly two-third (82/131, 62.6%) of the participants were willing to participate in one or more hypothetical digital health interventions. CONCLUSIONS: Social media is a feasible and cost-effective method for reaching justice-involved young adults to participate in substance use research trials. With limited budgets, researchers can reach a broad audience, many of whom could benefit from treatment but are not currently engaged in care. Proposed digital health interventions focusing on reducing substance use, such as private Facebook groups, SMS text message-based appointment reminders, and coaching, had high acceptability. Future work will build on these findings to develop substance use treatment interventions for this population.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».