The Building Emotional Awareness and Mental health (BEAM) program developed with a community partner for mothers of infants: A study protocol for a pilot randomized controlled trial
Bibliographic record
Abstract
Background: Drastic increases in the rates of maternal depression and anxiety have been reported since the COVID-19 pandemic began. Most programs aim to improve maternal mental health or parenting skills separately, despite it being more effective to target both concurrently. The Building Emotional Awareness and Mental health (BEAM) program was developed to address this gap. BEAM is a mobile health program aiming to mitigate the impacts of pandemic stress on family well-being. Since many family agencies lack infrastructure and personnel to adequately treat maternal mental health concerns, a partnership will occur with Family Dynamics (a local family agency) to address this unmet need. This study aims to examine (1) the influence of BEAM, vs. standard of care, at reducing negative mental health, parenting, and child outcomes and (2) the engagement, feasibility, and acceptability of BEAM, vs. standard of care, while exploring opportunities for program development. Methods: A randomized controlled trial will compare 10 weeks of the BEAM program to standard of care among 160 (80 per group) mothers who have depression and/or anxiety and children 6-18 months old living in Manitoba, Canada. Primary (maternal depression [Patient Health Questionnaire-9] and/or anxiety [Generalized Anxiety Disorder-7]), secondary (other maternal mental health, parenting, and child outcomes), and exploratory (e.g., relationship functioning) outcomes will be self-reported at three time-points. Google Analytics and back-end App data will be used to assess the engagement, feasibility, and acceptability of the BEAM program. A longitudinal analysis of covariance, using linear mixed modeling, will test treatment effects on outcomes of interest. Discussion: In partnership with a local family agency, BEAM holds the potential to promote maternal-child health via a cost-effective and easily accessible program designed to scale. Results may inform care and increase access to first line care for families typically waiting 12+ months to access mental health and parenting services. Trial Registration: This trial was retrospectively registered with ClinicalTrial.gov (NCT05398107) on May 31st, 2022. https://clinicaltrials.gov/ct2/show/NCT05398107
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".