Fitness, fatness and self concept in children
Bibliographic record
Abstract
Background: Among children, both cardiorespiratory fitness and relative weight have been shown to be associated with cognitive function, mental health, and self-concept. However, it is not well understood how different combinations of fitness and fatness are related to these outcomes. Purpose: This study investigates the impact of the Fit-Fat relationship on global self-worth and perceived athletic competence in children. Methods: Participants were 2134 children (50% M), with a mean age of 11.3 (SD=0.4). Participants completed body composition measures, the Leger Shuttle Run Test (VO2max) and the Harter GSW and AC subscales. Fit-fat was defined as VO2max/(waist/height). Four models were defined for each outcome; one for each predictor separately (VO2max, waist:height, fit-fat) and one with all three predictors. In the final model, variables were orthogonalized to measure the contribution of Fit-Fat beyond its constituent measures. Results: Fit-Fat (AŸ= .20, p < .001) and VO2max (AŸ= .19, p < .001) were equivalently associated with self-worth, while VO2max (AŸ = .34, p < .001) was superior to Fit-Fat (AŸ = .26, p < .001) as an indicator of athletic competence. Effects for waist:height were slightly weaker in both cases. For both outcomes, Fit-Fat made a small but significant contribution after effects of VO2max and waist:height were removed (self-worth: AŸ=0.04, p=0.05; competence, AŸ=0.05, p=0.01). Conclusion: While Fit-Fat is a useful indicator of self-worth and athletic competence, it is not superior to its constituent variables. However, it captures variance not accounted for by CRF or body composition measures, and is therefore of independent importance. Word Count: 249/250
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".