Digital Training for Mental Health Promotion in Young People With Climate Change-Related Distress: Protocol for a Feasibility Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Efforts in mental health research have long focused on the care and long-term outcomes of mental disorders. More recently, a shift in focus has occurred toward mental health promotion and prevention. One priority target population for promotion and prevention is youth with climate change-related distress. In light of the real-world threat of climate change, adaptive emotion regulation and engagement in meaningful action are 2 important strategies for promoting mental health. Ecological momentary interventions (EMIs) allow for the delivery of accessible interventions for young people with climate change-related distress, but evidence on their feasibility or beneficial effects is currently lacking. OBJECTIVE: We aimed to examine the feasibility and initial signals of efficacy of the Climate Mind and Act (CliMACT) training, a novel hybrid EMI for mental health promotion in youth with climate change-related distress. METHODS: A 2-arm, parallel-group, and assessor- and analyst-blinded feasibility randomized controlled trial (RCT) will be conducted in 50 young people aged 14-25 years with climate change-related distress, who will be allocated on a 1:1 ratio to the experimental condition (CliMACT training + care as usual [CAU]) or the control condition (CAU only). CliMACT involves 3 sessions with a mental health professional and 6-week access to a smartphone-based EMI to support the real-world transfer of training content based on compassion-focused interventions and acceptance and commitment therapy. The EMI delivery schemes involve enhancing (introducing new EMI components), consolidating (training of EMI components), and adaptive (triggered in moments of higher negative affect) components. CAU involves access to all standard health care and social services. Feasibility criteria of the trial methodology include recruitment, randomization, and retention. Feasibility outcomes of delivering the CliMACT training include participant satisfaction, participant adherence, and mental health professionals' fidelity to the training protocol. Initial signals of efficacy on mental health candidate outcomes and mechanisms will be explored. As feasibility criteria for a priori planned subgroup analyses, credibility criteria will be established and distributions of indicators for health inequities explored. Feasibility criteria for measuring costs of care and service use and health-related quality of life for an economic evaluation in a future definitive RCT will include exploring response distributions across groups. Candidate outcomes and mechanisms will be assessed at baseline, post training, and 4-week follow-up, using self-report and 6 days of ecological momentary assessment. RESULTS: The first enrollment took place in December 2024. Data collection was completed by August 25, 2025. Results are expected for publication in 2026. CONCLUSIONS: To our knowledge, this is the first study to establish the feasibility and initial signals of efficacy of an EMI, targeted specifically at young people with climate change-related distress. If feasibility can be established, the trial will inform a future fully powered efficacy-effectiveness RCT, accompanied by an economic evaluation. TRIAL REGISTRATION: ISRCTN ISRCTN33613914; https://doi.org/10.1186/ISRCTN33613914. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77764.
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 | 0,040 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,011 | 0,006 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,112 | 0,018 |
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 ».