Feasibility of the Building Emotional Awareness and Mental health (BEAM) eHealth Program for Mothers of Infants when Delivered in Partnership with a Community Collaborator: A Pragmatic Randomized Controlled Trial
Bibliographic record
Abstract
Background: Maternal depression and anxiety dramatically increased for mothers of young children during the COVID-19 pandemic. Few programs target maternal mental health and parenting skills concurrently despite this being more effective. An eHealth program, Building Emotional Awareness and Mental health (BEAM) was developed to address this gap. A feasibility randomized controlled trial (RCT) was conducted with BEAM with a community partner versus an existing App for adult mental health (MoodMission). Methods: Mothers of children 6-18 months old participated in a two-arm, parallel-design pragmatic RCT (April-June 2022). Mothers were randomly allocated to BEAM or MoodMission. Primary (depression, anxiety) and secondary (anger, sleep disturbance, parenting stress) outcomes were collected at pre-intervention, post-intervention, and 6-month follow-up. Results: Eighty mothers (Mage = 31.61 years old) were enrolled. All feasibility metrics were met based on a priori benchmarks for success. All maternal mental health and parenting stress outcomes decreased from pre- to post-intervention and follow-up in BEAM and MoodMission. When controlling for symptom severity at time of enrollment, treatment group (BEAM, MoodMission) moderated anxiety symptom reductions such that BEAM was more effective in reducing anxiety symptoms across time versus MoodMission (b = -1.48, SE = 0.74, p = .045). Symptom severity at enrollment did not moderate symptom reductions and there was no three-way interaction between symptom severity*treatment group*time. Discussion: Feasibility and effectiveness of BEAM was demonstrated for delivery with a community partner. Findings suggest BEAM holds the potential to promote the mental well-being of mothers of young children. Results have informed a future large-scale RCT across Canada. Trial Registration: ClinicalTrial.gov (NCT05 398107).
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".