Exploring profiles of fathers integrating food and physical activity parenting practices
Bibliographic record
Abstract
Abstract Objective: This study aims to identify fathers’ profiles integrating food parenting practices (FPP) and physical activity parenting practices (PAPP). Design: We analysed cross-sectional data. The fathers completed the reduced FPP and PAPP item banks and socio-demographic and family dynamics (co-parenting and household responsibility) questionnaires. We identified fathers’ profiles via latent profile analysis. We explored the influence of social determinants, child characteristics and family dynamics on fathers’ profiles using multinomial logistic regression. Setting: Online survey in the USA. Participants: Fathers of 5–11-year-old children. Results: We analysed data from 606 fathers (age = 38 ± 8·0; Hispanic = 37·5 %). Most fathers self-identified as White (57·9 %) or Black/African American (17·7 %), overweight (41·1 %) or obese (34·8 %); attended college (70 %); earned > $47 000 (62·7 %); worked 40 hrs/week (63·4 %) and were biological fathers (90·1 %). Most children (boys = 55·5 %) were 5–8 years old (65·2 %). We identified five fathers’ profiles combining FPP and PAPP: (1) Engaged Supporter Father (n 94 (15·5 %)); (2) Leveled Father (n 160 (26·4 %)); (3) Autonomy-Focused Father (n 117 (19·3 %)); (4) Uninvolved Father (n 113 (18·6 %)) and (5) Control-Focused Father (n 122 (20·1 %)). We observed significant associations with race, ethnicity, child characteristics, co-parenting and household responsibility but not with education level, annual income or employment status. We observed significant pairwise differences between profiles in co-parenting and household responsibility, with the Engaged Supporter Father presenting higher scores in both measures. Conclusions: Understanding how fathers’ FPP and PAPP interact can enhance assessments for a comprehensive understanding of fathers’ influences on children’s health. Recognising the characteristics and differences among fathers’ profiles may enable tailored interventions, potentially improving children’s health trajectories.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".