The Efficacy of a Mental Health and Parenting Intervention (BEAM) and the Role of Romantic Relationship Quality: How do Partners Influence Treatment Response?
Bibliographic record
Abstract
Background: Parental mental health concerns are common during the first few years following childbirth and are associated with adverse consequences for mothers and their children. The COVID-19 pandemic contributed to elevated levels of maternal mental health problems and introduced unique parenting stressors, highlighting the urgent need for accessible digital mental health interventions. There is a robust association between the quality of romantic relationships and psychopathology, and romantic relationship quality can influence the effectiveness of individual mental health interventions. Therefore, the current dissertation 1) evaluated the preliminary effectiveness and feasibility of a novel app-based program for mothers of infants and toddlers presenting with depression and/or anxiety (BEAM), 2) discussed program revisions, and 3) assessed the association between relationship factors and treatment effectiveness. Methods: The first manuscript was a pilot study of the BEAM program. The transition chapter discusses adaptations to the BEAM program and observations from the initial study that helped to inform the second manuscript. The second manuscript examined relationship factors as moderators of treatment outcomes using a phase-III longitudinal RCT. Results: The first manuscript (n = 46) demonstrated moderate levels of feasibility and strong preliminary efficacy of the BEAM program, with significant reductions in maternal depression, anxiety, parenting stress, and child internalizing symptoms. The second manuscript (n = 116) found that there was a high proportion of individuals who reported notable relationship distress (60%). Better overall partner support and satisfaction were associated with less mental health concerns. Moderation analyses revealed that BEAM contributed to greater reductions in overreactive discipline for mothers with lower levels of partner support or relationship satisfaction at baseline and that BEAM contributed to greater reductions in anxiety and anger at 6-month follow-up for mothers with lower levels of partner information support at baseline. Conclusion: The BEAM program shows promise as an accessible and scalable digital intervention for mothers. Many individuals report relationship dissatisfaction, and those with lower satisfaction and support appeared to benefit more from BEAM. Findings will help direct the program to the most suitable populations. Implementing relationship enhancement strategies may be a future direction to address this population's high levels of relationship dissatisfaction.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".