Evaluating a Mobile App Supporting Evidence-Based Parenting Skills: Thematic Analysis of Parent Experience
Bibliographic record
Abstract
Background: Disruptive behavior disorders are among the most common disorders of childhood, and evidence-based parenting programs are the first-line treatment. Digital microinterventions have been proposed as one possible means of supporting parenting style change by giving parents in-the-moment advice about how to respond to challenging behavior. Until now, no digital microintervention supporting evidence-based parenting skills programs has been evaluated. Objective: The aim of this study is to evaluate the subjective experience of parents using a digital microintervention to support evidence-based parenting skills, with particular attention to acceptability, usability, family relationships, and parents' values. Methods: We conducted serial interviews with 11 parents of 33 children before and after spending 3 weeks using an app including 3 digital microinterventions. Parents were recruited via local authorities in the Midlands region of the United Kingdom. Previous participation in a parenting program was an inclusion criterion. Interviews explored family composition; child behavior problems; and experience of using the mobile app, including barriers to use. Thematic analysis was conducted from a user-centered design perspective, and illustrative case vignettes were produced. Results: Many parents used the app in ways that helped them rather than strictly following the instructions they were given. Parents described a range of barriers to using the app including practical problems and failure to change child behavior. Parents and children responded in a variety of ways to the use of the phone, with many wholeheartedly embracing the convenience of technology. Case vignettes illustrate the uniqueness of each family's experience. Conclusions: Parents' use of a mobile app supporting evidence-based parenting skills is difficult to predict due to the unique challenges each family encounters. Many parents found it an acceptable and helpful addition to family life, but increased personalization is likely to be key to supporting parents. Future digital microintervention developers should keep in mind that parents are likely to use the app pragmatically rather than following instructions, may struggle to use a complex app under pressure, and are likely to hold complex feelings about parenting with an app.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".