Training Vegetable Parenting Practices Through a Mobile Game: Iterative Qualitative Alpha Test
Bibliographic record
Abstract
BACKGROUND: Vegetable consumption protects against chronic diseases, but many young children do not eat vegetables. One quest within the mobile application Mommio was developed to train mothers of preschoolers in effective vegetable parenting practices, or ways to approach getting their child to eat and enjoy vegetables. A much earlier version of the game, then called Kiddio, was alpha tested previously, but the game has since evolved in key ways. OBJECTIVE: The purpose of this research was to alpha test the first quest, substantiate earlier findings and obtain feedback on new game features to develop an effective, compelling parenting game. METHODS: Mothers of preschool children (n=20) played a single quest of Mommio 2 to 4 times, immediately after which a semi-structured interview about their experience was completed. Interviews were transcribed and double coded using thematic analysis methods. RESULTS: Mothers generally liked the game, finding it realistic and engaging. Some participants had difficulties with mechanics for moving around the 3-D environment. Tips and hints were well received, and further expansion and customization were desired. CONCLUSIONS: Earlier findings were supported, though Mommio players reported more enjoyment than Kiddio players. Continued development will include more user-friendly mechanics, customization, opportunities for environment interaction, and food parenting scenarios.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".