Practicality of the My Baby Now App for Fathers by Fathers: Qualitative Case Study
Bibliographic record
Abstract
BACKGROUND: Evolving societal trends are resulting in fathers having an increasing influence on the health-related behaviors that children develop. Research shows that most fathers are committed to their role and when equipped with knowledge, can have a positive impact on their child's health. However, parenting resources typically target mothers, with fathers being excluded. While evolving mobile phone technology provides an efficient means for delivering parenting resources, many fathers find that mobile health (mHealth) technology does not provide material they can engage with. OBJECTIVE: This study aimed to explore how to make parenting apps more engaging and useful for fathers using an existing parenting mHealth resource, the My Baby Now app, as a case study. METHODS: A total of 14 purposefully selected, Australian fathers of 7 months to 5-year-old children took part in a qualitative study, comprising either focus groups or interviews. Recorded focus groups and interviews were transcribed verbatim, then coded using a combination of deductive and inductive methods. Reflexive thematic analysis was undertaken to identify patterns and themes. RESULTS: Current parenting apps provide parenting information that can be unappealing for fathers. To improve paternal engagement with mHealth resources, fathers highlighted the need for father specific information, with an increase in positive imagery and positive descriptions of fathers in their parenting role. There should be father-exclusive domains such as forums, and also push notifications to provide positive reinforcement and encouragement for fathers. CONCLUSIONS: mHealth has the capacity to deliver information to fathers when needed. This reduces the risk of paternal frustration and disengagement from parenting. Further benefit will be gained by research to understand possible differences in mHealth app usage by fathers of differing socioeconomic position, cultural backgrounds, and family status, such as single fathers and same-sex couples.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".