Global Implications From the Rise and Recession of Telehealth in Aotearoa New Zealand Mental Health Services During the COVID-19 Pandemic: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic accelerated the adoption of telehealth services for remote mental health care provision. Although studies indicate that telehealth can enhance the efficiency of service delivery and might be favored or even preferred by certain clients, its use varied after the pandemic. Once the pandemic-related restrictions eased, some regions curtailed their telehealth offerings, whereas others sustained them. Understanding the factors that influenced these decisions can offer valuable insights for evidence-based decision-making concerning the future of telehealth in mental health services. OBJECTIVE: This study explored the factors associated with the uptake of and retreat from telehealth across a multiregional outpatient mental health service in Aotearoa New Zealand. We aimed to contribute to the understanding of the factors influencing clinicians' use of telehealth services to inform policy and practice. METHODS: Applying an interpretive description methodology, this sequential mixed methods study involved semistructured interviews with 33 mental health clinicians, followed by a time-series analysis of population-level quantitative data on clinician appointment activities before and throughout the COVID-19 pandemic. The interviews were thematically analyzed, and select themes were reframed for quantitative testing. The time-series analysis was conducted using administrative data to explore the extent to which these data supported the themes. In total, 4,117,035 observations were analyzed between September 2, 2019, and August 1, 2022. The findings were then synthesized through the rereview of qualitative themes. RESULTS: The rise and recession of telehealth in the study regions were related to 3 overarching themes: clinician preparedness and role suitability, population determinants, and service capability. Participants spoke about the importance of familiarity and training but noted differences between specialist roles. Quantitative data further suggested differences based on the form of telehealth services offered (eg, audiovisual or telephone). In addition, differences were noted based on age, gender, and ethnicity; however, clinicians recognized that effective telehealth use enabled clinicians' flexibility and client choice. In turn, clinicians spoke about system factors such as telehealth usability and digital exclusion that underpinned the daily functionality of telehealth. CONCLUSIONS: For telehealth services to thrive when they are not required by circumstances such as pandemic, investment is needed in telehealth training for clinicians, digital infrastructure, and resources for mental health teams. The strength of this study lies in its use of population-level data and consideration of a telehealth service operating across a range of teams. In turn, these findings reflect the voice of a variety of mental health clinicians, including teams operating from within specific cultural perspectives.
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 | 0,009 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| 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 ».