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Record W4408029048 · doi:10.2196/68807

Augmenting Parenting Programs With the Pause Mobile App: Mixed Methods Evaluation

2025· article· en· W4408029048 on OpenAlexvenueno aff
Nathan Hodson, Peter Woods, Stephen Donohoe, Juan Luque Solano, Manuel Giardino, Michael Sobolev, Domenico Giacco

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintMobile appsPsychologyComputer scienceInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Parenting programs are the recommended treatment for common mental health problems of childhood such as conduct disorder. In the United Kingdom, local authorities have responsibility for providing or commissioning these programs through face-to-face and video call weekly groups and e-learning style asynchronous offerings. However, there has been a shortage of research into the potential of digital resources to augment and enhance parenting groups. OBJECTIVE: This pilot study aimed to explore whether providing digital microinterventions in a mobile app (Pause) to augment parenting programs is a feasible strategy. Pause fits into parenting programs and prompts and supports parents to use each week's new parenting skill at home. Specifically, we want to understand (1) whether parents use Pause, (2) what type of features or tools in Pause are most frequently used for support, and (3) what are the perceived strengths and weaknesses of Pause. METHODS: Pause was provided to parents attending 3 of the most common parenting programs delivered across 3 local authorities in the United Kingdom. During weekly sessions, parents were supported to add "tools" in the app, which mapped onto the training in their session, for example, distracting their child, setting age-appropriate consequences, and using praise. Preprogram surveys were obtained at the first session. After programs were completed, postprogram surveys were administered to measure app use, gather which tools parents used, and explore the strengths and weaknesses of the app. Participants and practitioners were invited for interviews, where the strengths and weaknesses of augmenting parenting programs with Pause were discussed in more detail. RESULTS: In total, 53 parents were recruited from groups. A total of 25 of 53 (47%) parents completed postsurveys distributed at their final parenting group session, in keeping with typical rates of attrition in parenting programs. In addition, 7 parents and 3 practitioners agreed to interviews after the program. Most of the parents (23/25, 92%) had used Pause. Other than the journal, used by 17 parents, the most popular tools were the relax tool and praise tool, each used by 10 parents. Survey data revealed specific strengths and weaknesses of the tools in Pause, particularly highlighting that parents wanted Pause to provide more ideas for distraction or relaxation activities. Interviews revealed the challenges parents attending programs face, the range of family members using Pause, and the diverse settings where it was used. Interviews also revealed specific opportunities for improving the user interface and for addressing challenges in the journaling function. CONCLUSIONS: This pilot study found good acceptability and engagement with Pause. Interviews revealed promising evidence, suggesting that Pause may improve family life and aid child behavior change. Future research should evaluate whether adding Pause to parenting programs increases their positive effects on children's behavior and mental health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.059
GPT teacher head0.455
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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