Weight control intentions and mental health among Canadian adolescents: a gender-based analysis of students in the COMPASS study
Bibliographic record
Abstract
INTRODUCTION: Little is known about gender differences in associations between weight control intentions and mental health in adolescents. Our objective was to examine these associations in a large sample of adolescent girls and boys. METHODS: Using data from Year 6 (2017-18) of the COMPASS study (n = 57 324), we performed a series of multivariable linear regressions to examine whether weight control intentions (gain, lose, stay the same, no intention) were associated with depression, anxiety and self-concept, while adjusting for relevant covariates including body mass index. Models were stratified by self-reported gender. RESULTS: Compared to those with no intentions, girls who intended to lose weight reported higher symptoms of depression (B = 0.52, p < 0.001) and anxiety (B = 0.41, p < 0.001) and poorer self-concept (B = 2.06, p < 0.001). Girls who intended to gain weight also reported higher symptoms of depression (B = 0.54, p < 0.001), anxiety (B = 0.50, p < 0.001) and self-concept (B = 1.25, p < 0.001). Boys who intended to lose weight reported greater symptoms of depression (B = 0.26, p < 0.001) and anxiety (B = 0.33, p < 0.001) and poor self-concept (B = 1.10, p < 0.001). In boys, weight-gain intentions were associated with greater symptoms of anxiety (B = 0.17, p < 0.05), but not depression or self-concept. CONCLUSION: Intentions to gain or lose weight were associated with symptoms of mental disorder and poor self-concept in our large sample of adolescents, and these relationships differed in boys and girls. These findings have important implications for school-based programs promoting healthy weight and body image.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".