Assessing the Care Modality Preferences and Predictors for Digital Mental Health Treatment Seekers in a Technology-Enabled Stepped Care Delivery System: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Access to mental health services continues to be a systemic problem in the United States and around the world owing to a variety of barriers including the limited availability of skilled providers and lack of mental health literacy among patients. Individuals seeking mental health treatment may not be aware of the multiple modalities of digital mental health care available to address their problems (eg, self-guided and group modalities, or one-to-one care with a provider). In fact, one-to-one, in-person treatment is the dominant care model with a masters- or doctoral-level trained mental health provider, and it may or may not be the appropriate or preferred level of care for an individual. Technology-enabled mental health platforms may be one way to improve access to mental health care by offering stepped care, but more research is needed to understand the care modality preferences of digital mental health care seekers because additional modalities become increasingly validated as effective treatment options. OBJECTIVE: The purpose of this study was to describe and evaluate the predictors of care modality preferences among individuals enrolled in a technology-enabled stepped mental health care platform. METHODS: This exploratory, cross-sectional study used employee data from the 2021 Modern Health database, an employer-sponsored mental health benefit that uses a technology-enabled platform to optimize digital mental health care delivery. Chi-square tests and one-way analysis of variance (ANOVA) were conducted to evaluate associations among the categorical and continuous factors of interest and the preferred care modality. Bivariate logistic regression models were constructed to estimate the odds ratios (ORs) of preferring a one-on-one versus self-guided group, or no preference for digital mental health care modalities. RESULTS: Data were analyzed for 3661 employees. The most common modality preference was one-on-one care (1613/3661, 44.06%). Approximately one-fourth of the digital mental health care seekers (881/3661, 24.06%) expressed a preference for pursuing self-guided care, and others (294/3661, 8.03%) expressed a preference for group care. The ORs indicated that individuals aged 45 years and above were significantly more likely to express a preference for self-guided care compared to individuals aged between 18 and 24 years (OR 2.47, 95% CI 1.70-3.59; P<.001). Individuals screening positive for anxiety (OR 0.73, 95% CI 0.62-0.86; P<.001) or depression (OR 0.79, 95% CI 0.66-0.95; P=.02) were more likely to prefer one-on-one care. CONCLUSIONS: Our findings elucidated that care modality preferences vary and are related to clinical severity factors and demographic variables among individuals seeking digital mental health care.
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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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».