Gamifying Parenting Education Using an App Developed for Pacific and Other New Zealand Families (Play Kindly): Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Play Kindly is a gamified animated app designed to address common behavioral problems in childhood. The interface is designed to appeal to Pacific people, a population group with a higher risk of developing clinically significant behavioral problems than most other ethnic groups in New Zealand. OBJECTIVE: The aim of this study is to explore the opinions of parents and professionals about the acceptability, usability, and content of Play Kindly. METHODS: We used qualitative and Pacific and Māori research methodologies. A total of five focus groups with 45 parents and 12 individual interviews with professionals were conducted. The five focus groups consisted of 2 pan-Pacific groups, 1 Māori group, 1 open group, and 1 group of young Pacific adults or prospective parents. The professionals were from a range of disciplines, and the majority had expertise in early childhood, parenting interventions, or research in this field. RESULTS: Play Kindly appealed to both parents and professionals. Participants related to the scenarios, which were created in collaboration with a playwright and animator. Although most participants liked the Pacific feel, there was some disagreement about how culturally specific the app should be. A range of issues with usability and gamification techniques were highlighted, likely attributed to the low budget and lack of initial co-design with parents as well as professionals with specific expertise in parenting. A number of parents and professionals felt that the parenting strategies were overly simplified and did not take into account the context in which the behavior occurred. Professionals suggested narrowing the focus of the app to deliver two important parenting messages: playing with your child and positively reinforcing desired behaviors. CONCLUSIONS: Play Kindly is the first culturally adapted parenting app of its kind designed for Pacific parents and other New Zealanders with children 2-5 years of age. This app has potential in Pacific communities where there are limited culturally specific parenting resources. The results of this study will guide improvements of the app prior to testing it in an open trial.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".