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Enregistrement W4321384889 · doi:10.2196/44064

Digital Technology Use and Mental Health Consultations: Survey of the Views and Experiences of Clinicians and Young People

2023· article· en· W4321384889 sur OpenAlexvenueno aff
Raphael Rifkin‐Zybutz, Nicholas Turner, Jane Derges, Helen Bould, Felicity Sedgewick, Rachael Gooberman‐Hill, Myles-Jay Linton, Paul Moran, Lucy Biddle

Notice bibliographique

RevueJMIR Mental Health · 2023
Typearticle
Langueen
DomainePsychology
ThématiqueDigital Mental Health Interventions
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute for Health and Care Research
Mots-clésMental healthThematic analysisDigital healthAnxietyMedicineDescriptive statisticsPsychologyPsychiatryHealth careQualitative research

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Digital technologies play an increasingly important role in the lives of young people and have important effects on their mental health. OBJECTIVE: We aimed to explore 3 key areas of the intersection between digital technology and mental health: the views and experiences of young people and clinicians about digital technology and mental health; implementation and barriers to the UK national guidance recommendation-that the discussion of digital technology use should form a core part of mental health assessment; and how digital technology might be used to support existing consultations. METHODS: Two cross-sectional web-based surveys were conducted in 2020 between June and December, with mental health clinicians (n=99) and young people (n=320). Descriptive statistics were used to summarize the proportions. Multilinear regression was used to explore how the answers varied by gender, sexuality, and age. Thematic analysis was used to explore the contents of the extended free-text answers. Anxiety was measured using the Generalized Anxiety Disorder Questionnaire-7 (GAD-7). RESULTS: Digital technology use was ubiquitous among young people, with positive and negative aspects acknowledged by both clinicians and young people. Negative experiences were common (131/284, 46.1%) and were associated with increased anxiety levels among young people (GAD-7 3.29; 95% CI 1.97-4.61; P<.001). Although the discussion of digital technology use was regarded as important by clinicians and acceptable by young people, less than half of clinicians (42/85, 49.4%) routinely asked about the use of digital technology and over a third of young people (48/121, 39.6%) who had received mental health care had never been asked about their digital technology use. The conversations were often experienced as unhelpful. Helpful conversations were characterized by greater depth and exploration of how an individual's digital technology use related to mental health. Despite most clinicians (59/83, 71.1%) wanting training, very few (21/86, 24.4%) reported receiving training. Clinicians were open to viewing mental health data from apps or social media to help with consultations. Although young people were generally, in theory, comfortable sharing such data with health professionals, when presented with a binary choice, most reported not wanting to share social media (84/117, 71.8%) or app data (67/118, 56.8%) during consultations. CONCLUSIONS: Digital technology use was common, and negative experiences were frequent and associated with anxiety. Over a third of young people were not asked about their digital technology use during mental health consultations, and potentially valuable information about relevant negative experiences on the web was not being captured during consultations. Clinicians would benefit from having access to training to support these discussions with young people. Although young people recognized that app data could be helpful to clinicians, they appeared hesitant to share their own data. This finding suggests that data sharing has barriers that need to be further explored.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,119
Score d'incertitude au seuil0,514

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,083
Tête enseignante GPT0,453
Écart entre enseignants0,370 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations25
Publié2023
Routes d'admission1
Résumé présentoui

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