Understanding action control of parental support behavior for child physical activity.
Bibliographic record
Abstract
OBJECTIVE: Parental support is the critical family-level variable linked to child physical activity (PA), yet the antecedents of support are poorly understood, and its relationship with intention is modest. The purpose of this study was to apply a framework designed to evaluate the intention-behavior gap, known as multiprocess action control (M-PAC), to understand parental support for regular child PA. METHOD: Mothers (N = 1,253) with children 5-12 years of age completed measures of attitudes, perceived control over support, behavioral regulation tactics (e.g., planning, self-monitoring), and intention to support. Over half (58%) reported on subsequent support behaviors 6 months later. RESULTS: Three intention-behavior profiles emerged: (a) nonintenders (26.4%; n = 331), (b) unsuccessful intenders (36.6%; n = 458), and (c) successful intenders (33%; n = 414). Congruent with M-PAC, a discriminant function analysis showed that affective attitude about support (r = .18), perceived behavioral control over support (r = .55), and behavioral regulation (r = .55) distinguished between all 3 intention-behavior profiles. A disaggregated analysis of specific behavioral regulation tactics showed that most distinguished all 3 profiles, yet planning, information seeking, and monitoring were the critical correlates of the discriminant function. CONCLUSION: The majority of mothers had positive intentions to support regular child PA, yet over half failed to enact this support. Difficulty of intention translating into support behavior arises from compromised control over support, self-regulation skills, and perceptions that the support experience is unenjoyable. Interventions aimed at strengthening these factors are recommended to improve parental support action control.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".