Parent eHealth Preferences: Perceived Credibility and Personal Reactions to AbilitiCBT, BEAM, and Triple P Online
Bibliographic record
Abstract
Parental eHealth needs and preferences are unknown. Evidence from in-person programs shows that programs that prioritize parent preferences have higher enrollment and adherence. Better knowledge of parental impressions and preferences based on current eHealth programs could help identify programs that are most in line with parental values, goals, and needs. Accordingly, the present study aimed to compare parental perceptions and preferences based on textual descriptions of three eHealth programs that have been prescribed to parents: AbilitiCBT (mental health-focused), BEAM (mental health and parenting-focused), and Triple P Online (parenting-focused). 177 parents of 0-5-year-old children in the United States were recruited through MTurk. Mental health symptoms in this sample were high (70.1% clinically concerning depression and/or anxiety symptoms and 74.6% clinically concerning parenting stress symptoms). Results showed that Triple P was less likely to be chosen than AbilitiCBT or BEAM; AbilitiCBT seemed more helpful to participants. There was considerable variability, and all programs were preferred by at least 17% of parents. Overall, the present study suggests that parents experiencing high psychological distress are less likely to choose to participate in a parenting program without mental health support and that it is important to offer diverse psychosocial service options to meet the needs of more parents. Further research is needed to identify specific program characteristics that parents prefer as well as parents’ rationale for their choices, which would help better tailor interventions to their preferences.
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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 | 0.004 | 0.019 |
| 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 | 0.001 |
| 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".