Building Emotional Awareness and Mental Health (BEAM): study protocol for a phase III randomized controlled trial of the BEAM app-based program for mothers of children 18–36 months
Bibliographic record
Abstract
BACKGROUND: The prevalence of maternal depression and anxiety has increased during the COVID-19 pandemic, and pregnant individuals are experiencing concerningly elevated levels of mental health symptoms worldwide. Many individuals may now be at heightened risk of postpartum mental health disorders. There are significant concerns that a cohort of children may be at-risk for impaired self-regulation and mental illness due to elevated exposure to perinatal mental illness. With both an increased prevalence of depression and limited availability of services due to the pandemic, there is an urgent need for accessible eHealth interventions for mothers of young children. The aims of this trial are to evaluate the efficacy of the Building Emotion Awareness and Mental Health (BEAM) app-based program for reducing maternal depression symptoms (primary outcome) and improve anxiety symptoms, parenting stress, family relationships, and mother and child functioning (secondary outcomes) compared to treatment as usual (TAU). METHODS: A two-arm randomized controlled trial (RCT) with repeated measures will be used to evaluate the efficacy of the BEAM intervention compared to TAU among a sample of 140 mothers with children aged 18 to 36 months, who self-report moderate-to-severe symptoms of depression and/or anxiety. Individuals will be recruited online, and those randomized to the treatment group will participate in 10 weeks of psychoeducation modules, an online social support forum, and weekly group teletherapy sessions. Assessments will occur at 18-36 months postpartum (pre-test, T1), immediately after the last week of the BEAM intervention (post-test, T2), and at 3 months after the intervention (follow-up, T3). DISCUSSION: eHealth interventions have the potential to address elevated maternal mental health symptoms, parenting stress, and child functioning concerns during and after the COVID-19 pandemic and to provide accessible programming to mothers who are in need of support. This RCT will build on an open pilot trial of the BEAM program and provide further evaluation of this evidence-based intervention. Findings will increase our understanding of depression in mothers with young children and reveal the potential for long-term improvements in maternal and child health and family well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT05306626 . Registered on April 1, 2022.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".