Participant Experiences of the Building Emotional Awareness and Mental Health App-Based Intervention: A Qualitative Program Evaluation
Bibliographic record
Abstract
Mothers with young children were disproportionately impacted by the COVID-19 pandemic. Maternal rates of clinically significant depression rapidly increased while mental health services simultaneously became less accessible. In response, we designed a novel app-based intervention, Building Emotional Awareness and Mental Health (BEAM), to address mental health concerns and promote supportive parenting in mothers. A total of 70 mothers participated in the BEAM pilot. Following completing the program, participants were invited to share feedback about their experience in BEAM through focus groups and a post-intervention questionnaire. The current study comprised a thematic analysis of qualitative focus group (n = 11) and questionnaire data (n = 40). Results demonstrate how participants were supported and benefited from the program (e.g., peer community, learning new skills and strategies) and suggestions for improvement. Findings highlight the acceptability of the BEAM program and can inform the development of future mental health app-based programs and services.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".