The Building Emotional Awareness and Mental health (BEAM) program developed with a community partner for mothers of infants: protocol for a feasibility 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. The study's objective is to examine the feasibility of the BEAM program when delivered with a community partner to inform a larger randomized controlled trial (RCT). METHODS: A pilot RCT will be conducted with mothers who have depression and/or anxiety with a child 6-18 months old living in Manitoba, Canada. Mothers will be randomized to the 10 weeks of the BEAM program or a standard of care (i.e., MoodMission). Back-end App data (collected via Google Analytics and Firebase) will be used to examine feasibility, engagement, and accessibility of the BEAM program; cost-effectiveness will also be examined. Implementation elements (e.g., maternal depression [Patient Health Questionnaire-9] and anxiety [Generalized Anxiety Disorder-7]) will be piloted to estimate the effect size and variance for future sample size calculations. DISCUSSION: In partnership with a local family agency, BEAM holds the potential to promote maternal-child health via a cost-effective and an easily accessible program designed to scale. Results will provide insight into the feasibility of the BEAM program and will inform future RCTs. TRIAL REGISTRATION {2A}: This trial was retrospectively registered with ClinicalTrial.gov ( NCT05398107 ) on May 31st, 2022.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".