Telehealth and In-Person Behavioral Health Services in Rural Communities Before and During the COVID-19 Pandemic: Multisite Prospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic triggered widespread adjustments across the US health care system. Telehealth use showed a substantial increase in mental health conditions and services due to acute public health emergency (PHE) behavioral health needs on top of long-standing gaps in access to behavioral health services. How health systems that were already providing behavioral telehealth services adjusted services and staffing during this period has not been well documented, particularly in rural areas with chronic shortages of behavioral health providers and services. OBJECTIVE: This study investigates patient and treatment characteristic changes from before the COVID-19 PHE to during the PHE within both telehealth and in-person behavioral health services provided in 95 rural communities across the United States. METHODS: We used a nonrandomized, prospective, multisite research design involving 2 active treatment groups. The telehealth cohort included all patients who initiated telehealth treatment regimens during the data collection period. A comparison group included a cohort of patients who initiated in-person treatment regimen. Patient enrollment occurred on a rolling basis, and data collection was extended for 3 months after treatment initiation for each patient. Chi-square tests compared changes from pre-PHE to PHE time periods within telehealth and in-person treatment cohorts. The dependent measures included patient diagnosis, clinicians providing treatment services, and type of treatment services provided at each encounter. The 4780 patients in the telehealth cohort and the 6457 patients in the in-person cohort had an average of 3.5 encounters during the 3-month follow-up period. RESULTS: The encounters involving anxiety, dissociative, and stress-related disorders in the telehealth cohort increased from 30% (698/2352) in the pre-PHE period to 35% (4632/12,853) in the PHE period (P<.001), and encounters involving substance use disorders in the in-person cohort increased from 11% (468/4249) in the pre-PHE period to 18% (3048/17,047) in the PHE period (P<.001). The encounters involving treatment service codes for alcohol, drug, and medication-assisted therapy in the telehealth cohort increased from 1% (22/2352) in the pre-PHE period to 11% (1470/13,387) in the PHE period (P<.001); likewise, encounters for this type of service in the in-person cohort increased from 0% (0/4249) in the pre-PHE period to 16% (2687/17,047) in the PHE period (P<.001). From the pre-PHE to the PHE period, encounters involving 60-minute psychotherapy in the telehealth cohort increased from 8% (190/2352) to 14% (1802/13,387; P<.001), while encounters involving group therapy in the in-person cohort decreased from 12% (502/4249) to 4% (739/17,047; P<.001). CONCLUSIONS: The COVID-19 pandemic challenged health service providers, and they adjusted the way both telehealth and in-person behavioral therapy services were delivered. Looking forward, future research is needed to explicate the interaction of patient, provider, setting, and intervention factors that influenced the patterns observed as a result of the COVID-19 pandemic.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| 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 | 0,001 | 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 ».