A Digital Peer Support Platform to Translate Web-Based Peer Support for Emerging Adult Mental Well-being: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mental health issues among emerging adults (aged 19-25 years) on a global scale have underscored the need to address their widespread experiences of depression and anxiety. As a result of the COVID-19 pandemic, emerging studies are being directed toward the development and deployment of digital peer emotional disclosure and support for the psychological well-being of emerging adults. However, it is important to explore the implementation and clinical effectiveness, as well as associated mechanisms of change, for optimal approaches in conducting digital peer support interventions for emerging adults' psychological well-being. OBJECTIVE: We describe a randomized controlled trial to evaluate the implementation and clinical effectiveness of Acceset, a digital peer support intervention to address emerging adult mental well-being. The intervention has 2 components. First, the digital peer support training equips befrienders (ie, peers who provide support) to harness 4 components of psychological well-being-mattering, selfhood, compassion, and mindfulness-to provide effective peer support for seekers (ie, peers who seek support). Second, Acceset incorporates psychological well-being digital markers and harnesses community engagement to drive emotional disclosure among peers. METHODS: A total of 100 participants (aged 19-25 years) from the National University of Singapore will be recruited and randomized into 2 arms. In arm 1 (n=50), the seekers will use Acceset with befrienders (n=30) as well as moderators (n=30) for 3 weeks. Arm 2 comprises a wait-listed control group (n=50). A questionnaire battery will be used to monitor seekers and befrienders at 4 time points. These include baseline (before the intervention), 3 weeks (end of the intervention), and 6 and 9 weeks (carryover effect measurement). Implementation outcomes of the intervention will involve evaluation of the training curriculum with respect to adoption and fidelity as well as user acceptability of the Acceset platform and its feasibility for broader deployment. Clinical outcomes will include mattering, selfhood, compassion, mindfulness, perceived social support, and psychological well-being scores. RESULTS: This protocol received National University of Singapore Institutional Ethics Review Board approval in October 2021. Recruitment will commence in January 2022. We expect data collection and analyses to be completed in June 2022. Preliminary findings are expected to be published in December 2022. The Cohen d index will be used for effect size estimation with a .05 (95% reliability) significance level and 80% power. CONCLUSIONS: This protocol considers a novel digital peer support intervention-Acceset-that incorporates components and digital markers of emerging adult mental well-being. Through the validation of the Acceset intervention, this study defines the parameters and conditions for digital peer support interventions for emerging adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT05083676; https://clinicaltrials.gov/ct2/show/NCT05083676. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34602.
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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.133 | 0.018 |
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".