Parent Preferences for Peer Connection in eHealth Programs
Bibliographic record
Abstract
Background Social support and connection with other parents are important factors associated with parental mental health and parenting practices. These social connections can be integrated in parental eHealth programs through forums or group therapy sessions, but parental needs and preferences regarding these eHealth features are unknown. Objective This study aims to examine parents’ preferences for connecting with other parents in eHealth programs. Methods In total, 162 parents of 0-5–year-old children in the United States were recruited through Amazon Mechanical Turk (MTurk; mean age 32.7, range 22-61 years; 80.2% White; 59.9% men, 39.5% women, and 0.6% nonbinary; 93.8% biological parents). Participants filled out a one-time survey. Best practice recommendations for using MTurk were employed (through captcha verification and attention checks). Descriptive statistics were run in SPSS (version 27; IBM Corp) on MacOS. Results Parents were asked to rate how likely they would be to use a digital program with weekly opportunities to connect with other parents in the program (1=very unlikely to 5=very likely). Overall, 13.4% of parents indicated that they would be (very) unlikely to use a program with that feature and 59.8% of them indicated that they would be (very) likely to use it, with the remaining 27.8% of them being neutral. On being asked specifically about their preference, 85% of parents indicated that they would prefer connecting with other parents in the program, with 70% of those preferring to connect anonymously. On a forum, 67% of parents indicated that they would be comfortable connecting with all parents (as opposed to mothers or fathers only); regarding videoconferencing, that number was 61%. Conclusions Considering that studies have shown the positive impact of social support for parental mental health and parenting practices, integrating anonymous connection with other parents should be considered in developing parental eHealth programs and would be in line with the preferences of most parents. Conflicts of Interest None declared.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".