Evaluating the Effectiveness of an E-Mental Health Intervention for People Living in Lebanon: Protocol for Two Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: The lack of availability of evidence-based services for people exposed to adversity globally has led to the development of psychological interventions with features that will likely make them more scalable. The evidence for the efficacy of e-mental health from high-income countries is compelling, and the use of these interventions could be a way to increase the coverage of evidence-based psychological interventions in low- and middle-income countries. Step-by-Step is a brief (5-session) intervention proposed by the World Health Organization as an innovative approach to reducing the suffering and disability associated with depression. OBJECTIVE: This study aims to evaluate the effectiveness and cost-effectiveness of a locally adapted version of Step-by-Step with Syrian nationals (trial 1) and Lebanese nationals and other populations residing in Lebanon (trial 2). METHODS: This Step-by-Step trial involves 2 parallel, two-armed, randomized controlled trials comparing the e-intervention Step-by-Step to enhanced care as usual in participants with depressive symptoms and impaired functioning. The randomized controlled trials are designed and powered to detect effectiveness in 2 populations: Syrians in Lebanon (n=568) and other people residing in Lebanon (n=568; Lebanese nationals and other populations resident in Lebanon). The primary outcomes are depressive symptomatology (measured with the Patient Health Questionnaire-9) and functioning (measured with the World Health Organization Disability Assessment Scale 2.0). Secondary outcomes include anxiety symptoms, posttraumatic stress disorder symptoms, personalized measures of psychosocial problems, subjective well-being, and economic effectiveness. Participants are mainly recruited through online advertising. Additional outreach methods will be used if required, for example through dissemination of information through partner agencies and organizations. They can access the intervention on a computer, tablet, and mobile phone through a hybrid app. Step-by-Step has 5 sessions, and users are guided by trained nonspecialist "e-helpers" providing phone-based or message-based support for around 15 minutes a week. RESULTS: The trials were funded in 2018. The study protocol was last verified June 20, 2019 (WHO ERC.0002797) and registered with ClinicalTrials.gov (NCT03720769). The trials started recruitment as of December 9, 2019, and all data collection was completed in December 2020. CONCLUSIONS: The Step-by-Step trials will provide evidence about the effectiveness of an e-mental health intervention in Lebanon. If the intervention proves to be effective, this will inform future scale-up of this and similar interventions in Lebanon and in other settings across the world. TRIAL REGISTRATION: ClinicalTrials.gov NCT03720769; https://clinicaltrials.gov/ct2/show/NCT03720769. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21585.
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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 | 0.076 | 0.075 |
| Meta-epidemiology (narrow) | 0.012 | 0.006 |
| Meta-epidemiology (broad) | 0.024 | 0.016 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.082 | 0.015 |
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".