Physical Activity Parenting Measurement and Research: Challenges, Explanations, and Solutions
Bibliographic record
Abstract
Physical activity (PA) parenting research has proliferated over the past decade, with findings verifying the influential role that parents play in children's emerging PA behaviors. This knowledge, however, has not translated into effective family-based PA interventions. During a preconference workshop to the 2012 International Society for Behavioral Nutrition and Physical Activity annual meeting, a PA parenting workgroup met to: (1) Discuss challenges in PA parenting research that may limit its translation, (2) identify explanations or reasons for such challenges, and (3) recommend strategies for future research. Challenges discussed by the workgroup included a proliferation of disconnected and inconsistently measured constructs, a limited understanding of the dimensions of PA parenting, and a narrow conceptualization of hypothesized moderators of the relationship between PA parenting and child PA. Potential reasons for such challenges emphasized by the group included a disinclination to employ theory when developing measures and examining predictors and outcomes of PA parenting as well as a lack of agreed-upon measurement standards. Suggested solutions focused on the need to link PA parenting research with general parenting research, define and adopt rigorous standards of measurement, and identify new methods to assess PA parenting. As an initial step toward implementing these recommendations, the workgroup developed a conceptual model that: (1) Integrates parenting dimensions from the general parenting literature into the conceptualization of PA parenting, (2) draws on behavioral and developmental theory, and (3) emphasizes areas which have been neglected to date including precursors to PA parenting and effect modifiers.
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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 | 0.415 | 0.487 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".