An App to Support Fathers’ Mental Health and Well-Being: User-Centered Development Study
Bibliographic record
Abstract
BACKGROUND: Numerous studies describe the popularity and usefulness of parenting programs. In particular, parenting programs are generally viewed as effective for supporting parents' mental well-being during key transition periods. However, the evidence base for fathers is limited owing to their lack of involvement in parenting programs and scarcity of tailored support. OBJECTIVE: This paper aimed to describe the co-design process for a universal digital intervention for fathers (fatherli) and the outline of a logic model with its expected outcomes. METHODS: Following established guidelines for co-designing and developing complex interventions, we conducted a nonsystematic review of the available literature to gather key information, developed market surveys to assess fathers' needs and interests, consulted with key stakeholders to obtain expert opinions, and engaged in a rapid iterative prototyping process with app developers. Each step was summarized, and the information was collated and integrated to inform a logic model and the features of the resulting intervention. RESULTS: The steps in the co-design process confirmed a need for and interest in a digital intervention for fathers. In response to this finding, fatherli was developed, consisting of 5 key features: a discussion forum for anyone to post information about various topics (the forum), a socializing platform for fathers to create and engage with others in small groups about topics or points of shared interest (dad hub), a tool for fathers to find other fathers with shared interests or within the same geographic location (dad finder), a resource for fathers to access up-to-date information about topics that interest them (dad wiki), and a portal to book sessions with coaches who specialize in different topics (dad coaching space). The evidence-based logic model proposes that if fatherli is successfully implemented, important outcomes such as increased parental efficacy and mental health help-seeking behaviors may be observed. CONCLUSIONS: We documented the co-design and development process of fatherli, which confirmed that it is possible to use input from end users and experts, integrated with theory and research evidence, to create suitable digital well-being interventions for fathers. In general, the key findings suggest that an app that facilitates connection, communication, and psychoeducation may appeal to fathers. Further studies will now focus on acceptability, feasibility, and effectiveness. Feedback gathered during pilot-testing will inform any further developments in the app to increase its applicability to fathers and its usability.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".