Predictors and impacts of engagement in an app-based social support forum: Exploring maternal depression, anxiety, and self-compassion
Bibliographic record
Abstract
Mothers with young children experienced elevated rates of depression and anxiety during the COVID-19 pandemic. Social support is a protective factor against depression and anxiety and is positively associated with indicators of mental well-being such as self-compassion. Social support contributes to mental well-being through improving appraisals of stressful events and mitigating feelings of isolation. Engaging in online social support forum communities may be an innovative avenue for mothers to receive social support and improve their mental well-being. However, little is known about the predictors and impacts of social support forum usage in mothers. In the present study mothers with depression and/or anxiety and a child 18-36 months old (N = 69 randomized) were invited to participate in a 10-week app-based mental health and parenting program called Building Emotional Awareness and Mental Health (BEAM). BEAM consisted of psychoeducational mental health and parenting videos, online telehealth group therapy, symptom monitoring, and a social support forum. Quantitative and qualitative methods were employed to explore predictors and impacts of engaging in the BEAM program forum. Pearson bivariate correlations revealed higher levels of education, income, and having more adults in the household (≥ 2) were associated with more forum engagement throughout the BEAM program. Pre-intervention mental health symptoms (i.e., depression, anxiety, self-compassion) were not associated with forum engagement. Multiple linear regressions revealed time spent on forum and number of posts made on the forum did not significantly predict change in participant depression, anxiety, or self-compassion scores pre- to post-intervention. Finally, a thematic analysis of post-intervention open-ended questionnaire data provided a detailed understanding of participant experiences using the forum. Themes derived demonstrate ways in which participants were supported by the forum (e.g., connecting with other mothers) and participant suggestions for forum improvement. The current research provides insights into who may engage in online support forums more frequently and provides preliminary information about the impact of forum use in mothers with depression and/or anxiety. Future research in this area to further elucidate the links between social support forum usage and mental health are suggested.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".