Psychotherapy Access Barriers and Interest in Digital Mental Health Interventions Among Adults With Treatment Needs: Survey Study
Notice bibliographique
Résumé
BACKGROUND: Digital mental health interventions (DMHIs) are a promising approach to reducing the public health burden of mental illness. DMHIs are efficacious, can provide evidence-based treatment with few resources, and are highly scalable relative to one-on-one face-to-face psychotherapy. There is potential for DMHIs to substantially reduce unmet treatment needs by circumventing structural barriers to treatment access (eg, cost, geography, and time). However, epidemiological research on perceived barriers to mental health care use demonstrates that attitudinal barriers, such as the lack of perceived need for treatment, are the most common self-reported reasons for not accessing care. Thus, the most important barriers to accessing traditional psychotherapy may also be barriers to accessing DMHIs. OBJECTIVE: This study aimed to explore whether attitudinal barriers to traditional psychotherapy access might also serve as barriers to DMHI uptake. We explored the relationships between individuals' structural versus attitudinal barriers to accessing psychotherapy and their indicators of potential use of internet-delivered guided self-help (GSH). METHODS: We collected survey data from 971 US adults who were recruited online via Prolific and screened for the presence of psychological distress. Participants provided information about demographic characteristics, current symptoms, and the use of psychotherapy in the past year. Those without past-year psychotherapy use (640/971, 65.9%) answered questions about perceived barriers to psychotherapy access, selecting all contributing barriers to not using psychotherapy and a primary barrier. Participants also read detailed information about a GSH intervention. Primary outcomes were participants' self-reported interest in the GSH intervention and self-reported likelihood of using the intervention if offered to them. RESULTS: Individuals who had used psychotherapy in the past year reported greater interest in GSH than those who had not (odds ratio [OR] 2.38, 95% CI 1.86-3.06; P<.001) and greater self-reported likelihood of using GSH (OR 2.25, 95% CI 1.71-2.96; P<.001). Attitudinal primary barriers (eg, lack of perceived need; 336/640, 52.5%) were more common than structural primary barriers (eg, money or insurance; 244/640, 38.1%). Relative to endorsing a structural primary barrier, endorsing an attitudinal primary barrier was associated with lower interest in GSH (OR 0.44, 95% CI 0.32-0.6; across all 3 barrier types, P<.001) and lower self-reported likelihood of using GSH (OR 0.61, 95% CI 0.43-0.87; P=.045). We found no statistically significant differences in primary study outcomes by race or ethnicity or by income, but income had a statistically significant relationship with primary barrier type (ORs 0.27-3.71; P=.045). CONCLUSIONS: Our findings suggest that attitudinal barriers to traditional psychotherapy use may also serve as barriers to DMHI use, suggesting that disregarding the role of attitudinal barriers may limit the reach of DMHIs. Future research should seek to further understand the relationship between general treatment-seeking attitudes and attitudes about DMHIs to inform the design and marketing of DMHIs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».