Relating Use of Effective Responsive, Structure, and Non-Directive Control Vegetable Parenting Practices to Subscales from the Model of Goal Directed Behavior
Bibliographic record
Abstract
Parents may positively influence children’s vegetable consumption through effective vegetable parenting practices (VPP). Research has demonstrated three dimensions of effective VPP: Effective Responsiveness, Structure, and Non-Directive Control, but there is limited research investigating each separately. This study presents the modeling of Effective Responsive, Structure, and Non-Directive Control VPP using constructs from the Model of Goal Directed Vegetable Parenting Practices (MGDVPP). Parents (n=307) completed a survey on demographics, MGDVPP constructs, and effective VPP. Block regression modeling tested three models: one for each dimension of effective VPP as the dependent variable. Independent variables included validated subscales representing MGDVPP constructs: Intention, Desire, Perceived Barriers, Autonomy, Relatedness, Self-Efficacy, Habit, Anticipated Emotions, Perceived Behavioral Control, Attitudes, and Norms. Participants were racially diverse, and a majority was female, of higher socioeconomic status, and with a male child. Effective Responsive VPP was positively related to a Habit subscale. Effective Structure VPP was positively related to a Barrier, two Habit, and an Attitude subscales. Effective Non-Directive Control VPP was positively related to being a high school or GED graduate, having younger children, a Habit, and two Intentions subscales, and negatively related to an Intentions and a Perceived Behavioral Control subscales. The adjusted R2 for the Effective Responsive, Structure, and Non-Directive Control VPP models were 0.432, 0.310, and 0.515, respectively. This was the first study to relate constructs from a theoretical model to effective VPP dimensions. Research is needed to longitudinally assess the MGDVPP and test its utility in vegetable-related interventions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".