Scalable Technology for Adolescents and Youth to Reduce Stress in the Treatment of Common Mental Disorders in Jordan: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Young people in low- and middle-income countries encounter significant barriers to accessing mental health support due to various factors, including a substantial treatment gap and limited health care budgets allocated to mental health. Using innovative strategies, such as scalable digital self-help psychological interventions, offers a potential solution for improving access to mental health support. However, digital mental health interventions come with their own set of challenges, including issues related to low user engagement. Chatbots, with their interactive and engaging nature, may present a promising avenue for the delivery of these interventions. OBJECTIVE: This study aims to explore the effectiveness of a newly developed World Health Organization (WHO) digital mental health intervention, titled Scalable Technology for Adolescents and Youth to Reduce Stress (STARS). METHODS: A single-blind, 2-arm randomized controlled trial will be conducted nationally across Jordan. Participants will include 344 young adults, aged 18-21 years, currently residing in Jordan. Inclusion criteria are heightened levels of psychological distress as determined through the 10-item Kessler Psychological Distress Scale (≥20). Assessment measures will be conducted at baseline, 1-week post intervention, and 3-month follow-up. Following baseline assessments, eligible participants will be randomized to receive STARS or enhanced usual care. The primary outcomes are the reduction of symptoms of depression and anxiety (Hopkins Symptom Checklist, 25 subscales) at 3-month follow-up. Secondary outcomes include general functioning (WHO Disability Assessment Schedule 2.0), well-being (WHO-5 Well-Being Index), personal problems (Psychological Outcomes Profile), and agency (State Hope Scale subscale). RESULTS: The study was funded in January 2020 by the Research for Health in Humanitarian Crises Programme (Elhra) and recruitment for the trial started on July 16, 2023. As of November 15, 2023, we randomized 228 participants. CONCLUSIONS: This trial intends to contribute to the growing digital mental health evidence base by exploring technological solutions to address global public health challenges. Given the widespread use of technology globally, even in resource-constrained settings, and the high adoption rates among adolescents and young individuals, digital initiatives such as STARS present promising opportunities for the future of mental health care in low- and middle-income countries. TRIAL REGISTRATION: ISRCTN Registry ISRCTN10152961; https://www.isrctn.com/ISRCTN10152961. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54585.
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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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.013 |
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".