Sociodemographic Predictors of Engagement in an eHealth Parenting Program for Mothers with Elevated Depressive and Anxiety Symptoms
Bibliographic record
Abstract
Background. Electronic health (eHealth) programs are a promising avenue to enhance the accessibility of parenting interventions that aim to improve parent mental health. However, engagement in eHealth parenting programs can be variable and sociodemographic factors may help elucidate for whom there are barriers. Such work may inform specific components of future eHealth interventions and support the scalability and effectiveness of existing interventions for those experiencing barriers.Objective. The current study aims to investigate the extent to which sociodemographic factors predict various engagement metrics in a novel eHealth parenting program (The Building Emotional Awareness and Mental Health [BEAM]).Methods. A secondary data analysis of engagement metrics was conducted in two separate randomized controlled trials (RCT) of the BEAM program. A total of n = 110 mothers (Mage = 31.85 years old, SDage = 4.67) of young children participated across both trials. We examined the magnitude of the relationships between sociodemographic predictors with distinct metrics of engagement.Results. Program engagement was highest in the earlier weeks of the program and declined across later weeks (β = -.16, p < .001). Older mother age positively predicted several engagement metrics, including more time watching mental health videos (β = .34, p = .010) and parenting videos (β = .34, p = .010). Higher household income positively predicted more forum page views (β = .44, p < .001), number of BEAM sessions attended (β = .30, p = .006), and more weeks of BEAM program engagement (β = .35, p < .001). First time-parents engaged in fewer program weeks (β = -.25, p = .009). Number of children in the household, employment status, and living in an urban area did not significantly predict any program engagement metrics. Conclusions. Parent engagement varied throughout the program, and it may be beneficial to specifically target mid and later program engagement for participants. First-time and low-income parents may face additional barriers in participating in eHealth programs and benefit from additional support from the intervention team to bolster engagement. Additional research will be helpful in replicating and extending these findings with larger samples to optimize program engagement.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".