Virtual Delivery of Early Psychosis Care: Retrospective Cohort Study of Factors Associated With Initial Engagement
Notice bibliographique
Résumé
Background: There is evidence that virtual delivery of early psychosis intervention (EPI) is well received by youth and has benefits such as reported improvements in accessibility, convenience, and comfort; however, potential barriers remain, including the digital divide and privacy concerns. Although initial engagement in EPI services is important for long-term recovery, little is known about initial engagement in the context of virtual care and the role of health equity and service use factors. Objective: This study aimed to identify factors associated with attendance at the initial EPI consultation appointment when most were being delivered virtually. Methods: This retrospective cohort study used electronic medical record data from patients aged 16 to 29 years who were referred to a large EPI program. The EPI program received 301 unique referrals that met study eligibility criteria from April to December 2020. Self-reported demographic variables were derived from the Centre for Addiction and Mental Health's structured health equity form and included age, gender, racial and ethnic group, country of birth, and sexual orientation. Service use factors derived from clinical documentation included referral source, days to consultation, and attendance at the consultation appointment, which was the primary outcome. Comparisons were made with 2018 to 2019 data from 999 participants from the same site prior to virtual care implementation using chi-square tests for categorical variables, independent t tests for continuous variables, and binary logistic regression. Results: Patients had a mean age of 23.2 (SD 3.3) years, and 214 (71.1%) participants identified as male. Compared with pre-virtual care, there were significantly higher rates of inpatient referral (114/301, 37.9%) and lower rates of referral from outpatient and other providers (122/301, 40.5%) post-virtual care (χ22=18.7, P<.001), with a small effect size and moderately narrow CI (Cramér V=0.120, 95% CI 0.06 to 0.17). Following univariable tests and stepwise backward selection, identifying as Black (odds ratio 0.45, 95% CI 0.21 to 0.97) and being referred from the emergency department or bridging clinic (odds ratio 0.24, 95% CI 0.08 to 0.72) were associated with decreased odds of attendance at the consultation appointment in the final adjusted model. All tests were 2-sided with an α level of .05. Conclusions: This study is innovative in that it examines the self-reported health equity and service use factors that may contribute to nonattendance when most EPI appointments are delivered virtually, unlike previous studies that focused solely on differences in attendance rates. Although it was during the COVID-19 pandemic and may not be representative of virtual care in real-world practice, this study suggests that virtual care may improve initial engagement in EPI services; however, barriers to care still exist for Black patients and those referred from the emergency department. A hybrid model may improve connection to EPI, though targeted approaches are needed to bridge the digital divide and ensure that structurally marginalized and high-acuity patients have equitable access to care.
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 | 0,001 | 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 | 0,000 | 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 ».