Changes in the Clinical Practice of Mental Health Service Providers Throughout the COVID-19 Pandemic: Longitudinal Questionnaire Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic impacted the practices of most mental health providers and resulted in a rapid transition to providing telemental health services, changes that were likely related to stay-at-home policies as well as increased need for services. OBJECTIVE: The aim of this study was to examine whether these changes to practice have been sustained over time throughout the course of the COVID-19 pandemic and whether there are differences among mental health provider type and setting. We hypothesized that there would be an increase in the number of patients seen in person after the initial surge of the pandemic in spring 2020 and subsequent discontinuation of stay-at-home policies, though with continued implementation of telemental health services across settings. METHODS: This study surveyed 235 of the 903 mental health providers who responded to a survey in spring 2020 (Time point 1) and at a 1-year follow-up in spring 2021 (Time point 2). Differences in practice adjustments, factors related to telemental health, and number of patients seen were examined across provider type (social worker, psychologist, neuropsychologist) and setting (academic medical center [AMC], community mental health, private practice, and Veterans Affairs hospital). RESULTS: From Time point 1 to Time point 2, there was a small but significant increase in the overall number of providers who were implementing telehealth (191/235, 81% to 204/235, 87%, P=.01) and there was a significant decline in canceled or rescheduled appointments (25%-50% in 2020 to 3%-7% in 2021, P<.001). Psychologists and providers working at AMCs reported decreased difficulty with telehealth implementation (P<.001), and providers working at AMCs and in private practice settings indicated they were more likely to continue telehealth services beyond spring 2021 (P<.001). The percent of time working remotely decreased overall (78% to 59%, P<.001), which was most notable among neuropsychologists and providers working at an AMC. There was an overall increase in the average number of patients seen in person per week compared with earlier in the pandemic (mean 4.3 to 8.7, P<.001), with no change in the number of patients seen via telehealth (mean 9.7 to 9.9, P=.66). CONCLUSIONS: These results show that the rapid transition to telemental health at the onset of the COVID-19 pandemic in spring 2020 was sustained over the next year, despite an overall increase in the number of patients seen in person. Although more providers reported returning to working on-site, over 50% of providers continued to use a hybrid model, and many providers reported they would be more likely to continue telemental health beyond spring 2021. This suggests the continued importance and reliance on telemental health services beyond the acute pandemic phase and has implications for future policies regulating the availability of telemental health services to patients.
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| 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) | 0,001 | 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 ».