Father‐Focused Childhood Obesity Prevention Interventions: A Scoping Review
Bibliographic record
Abstract
Father-focused childhood obesity prevention interventions are emerging as novel strategies to tackle increasing childhood obesity rates. This scoping review aimed to describe the characteristics of these interventions and their participants to identify knowledge gaps and improve future interventions. A search for eligible interventions was conducted in MEDLINE (Ovid), Web of Science, and CINAHL (EBSCO) between 2023 and 2024 without date or geographic restrictions. Data were extracted using a tool developed for this review and analyzed using content analysis. Fourteen unique interventions were identified and were all conducted in developed Western countries (n = 14). Most were implemented between 2015 and 2019 (n = 9) in community settings (n = 13) and were feasibility and efficacy trials (n = 11). Most interventions targeted elementary school-aged (6-10 years) (n = 8) and middle school-aged (11-13 years) children (n = 8), followed by preschool-kindergarten-aged children (2-5 years) (n = 7); none targeted high school-aged children (14-17 years). Most interventions targeted physical activity parenting (n = 14) and sedentary behavior parenting (n = 13), with less focus on food parenting (n = 11) and sleep parenting (n = 0). Fathers from nontraditional families and racial/ethnic minority groups were underrepresented and underreported. Our findings underscore the need to increase the emphasis on food and sleep parenting in father-focused interventions and to include fathers from diverse backgrounds to address health disparities.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".