Brief Mobile App–Based Mindfulness Intervention for Indonesian Senior High School Teachers: Protocol for a Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has increased the level of anxiety among Indonesian senior high school teachers, who face challenges to treat their mental disorder symptoms that arise during their working hours, as mental health services in Indonesia are limited. Therefore, it is vital to equip schoolteachers in Indonesia with early interventions that are easily available, private, and affordable, and 1 feasible approach is to deploy a smartphone mobile app. OBJECTIVE: The objectives of this study are (1) to evaluate the feasibility of a brief mindfulness-based mobile app (BM-MA) for Indonesian senior high school teachers experiencing anxiety and stress and (2) to examine the effects of using the BM-MA on anxiety, stress, life satisfaction, self-efficacy, trait mindfulness, self-compassion, and physical and social dysfunction among the participants. METHODS: We followed the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 statement for this feasibility randomized controlled trial (RCT) protocol. A total of 60 Indonesian senior high school teachers were recruited for this study and randomly assigned to either the intervention group (BM-MA) or a wait-list control group (CG) in a 1:1 ratio. The BM-MA group was required to engage in mindfulness practices using the app for 10-20 minutes per day for 3 weeks. All participants were assessed with a battery of self-report measures at baseline, postintervention, and at 1-month follow-up. Validated scales used to measure the outcome variables of interest included the Satisfaction With Life Scale (SLS), the Teachers' Sense of Efficacy Scale (TSES), the Self-Compassion Scale-Short Form (SCS-SF), Generalized Anxiety Disorder-7 (GAD-7), General Health Questionnaire-12 (GHQ-12), and the Five Facet Mindfulness Questionnaire (FFMQ). The practicality and acceptability of the app will be evaluated using the Client Satisfaction Questionnaire-8 (CSQ-8) and structured qualitative interviews. Data from the interviews will be analyzed with the deductive thematic analysis framework as a process of qualitative inquiry. Repeated measures ANOVA with groups (intervention vs control) as a between-subject factor and time as a within-subject factor (baseline, postintervention, and 1-month follow-up) will be used to examine the effects of the BM-MA on the outcome variables. The data will be analyzed using an intent-to-treat approach and published in accordance with CONSORT (Consolidated Standards of Reporting Trials) recommendations. RESULTS: Participants were recruited in December 2023, and this pilot RCT was conducted from January through March 2024. Data analysis was conducted from March through May 2024. The results of this study are expected to be published in December 2024. The trial registration of this protocol was submitted to the Chinese Clinical Trial Registry. CONCLUSIONS: This study aims to determine the feasibility and efficacy of the BM-MA, a digital mental health intervention developed using an existing mindfulness-based app, and assess its potential for widespread use. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300068085; https://tinyurl.com/2d2x4bxk. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56693.
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 | 0,023 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,009 |
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 ».