Parent Preferences for Peer Connection in Virtual Mental Health and Parenting Support Platforms
Bibliographic record
Abstract
Abstract Peer connections can be integrated in online and app-based (eHealth) family mental health and parenting programs through forums/chats or video group sessions. Little is known about parental preferences regarding eHealth features, yet they could be key factors influencing uptake and utility of programs. Accordingly, the present study aims to examine parent preferences for connecting with other parents in eHealth programs. Parents ( n = 177) of 0–5-year-old children in the United States were recruited on MTurk. Parents were asked about peer connection preferences through questions framed around how and with whom they would like to connect when using a virtual mental health and parenting support platform. Most (86.4%) preferred connecting with other parents in an eHealth program with 73.2% preferring to connect anonymously. If using a forum, 45.5% of mothers were comfortable connecting only with other mothers whereas 54.5% were comfortable connecting with parents of any gender; 80.3% of fathers were comfortable connecting with all parents. Results were similar for videoconferencing. Age, income, number of children, recent stressful events, social support, mental health symptoms, and parenting stress did not predict any of these preferences. Our results suggest that integrating peer connection should be considered in developing parental eHealth programs as it may be in line with the preferences of most parents and programs that match user preferences have been shown to have higher enrollment and adherence. These preferences should be further studied with community samples and diverse participants to strengthen confidence in the findings and properly inform program development.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".