Brief Mobile App–Based Mindfulness Intervention for Indonesian Senior High School Teachers: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".