Building Emotional Awareness and Mental Health (BEAM): an open-pilot and feasibility study of a digital mental health and parenting intervention for mothers of infants
Bibliographic record
Abstract
BACKGROUND: Maternal mental health concerns and parenting stress in the first few years following childbirth are common and pose significant risks to maternal and child well-being. The COVID-19 pandemic has led to increases in maternal depression and anxiety and has presented unique parenting stressors. Although early intervention is crucial, there are significant barriers to accessing care. METHODS: To inform a larger randomized controlled trial, the current open-pilot trial investigated initial evidence for the feasibility, acceptability, and efficacy of a newly developed online group therapy and app-based mental health and parenting program (BEAM) for mothers of infants. Forty-six mothers 18 years or older with clinically elevated depression scores, with an infant aged 6-17 months old, and who lived in Manitoba or Alberta were enrolled in the 10-week program (starting in July 2021) and completed self-report surveys. RESULTS: The majority of participants engaged in each of the program components at least once and participants indicated relatively high levels of app satisfaction, ease of use, and usefulness. However, there was a high level of attrition (46%). Paired-sample t-tests indicated significant pre- to post-intervention change in maternal depression, anxiety, and parenting stress, and in child internalizing, but not externalizing symptoms. Effect sizes were in the medium to high range, with the largest effect size observed for depressive symptoms (Cohen's d = .93). DISCUSSION: This study shows moderate levels of feasibility and strong preliminary efficacy of the BEAM program. Limitations to program design and delivery are being addressed for testing in adequately powered follow-up trials of the BEAM program for mothers of infants. TRIAL REGISTRATION: NCT04772677 . Registered on February 26 2021.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".