Association between accuracy of weight perception and life satisfaction among adults with and without anxiety and mood disorders: a cross-sectional study of the 2015–2018 Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Weight status and weight perception have a significant impact on life satisfaction. As overweight prevalence increases in Canada, it is important to understand how accuracy of weight perception (AWP) is associated with life satisfaction. This study explored the association between AWP and life satisfaction among Canadian adults with and without anxiety and/or mood disorders. METHODS: Using data from the 2015-2018 cycles of the Canadian Community Health Survey, an indicator of AWP was created to capture concordance between perceived weight and actual weight status. Univariate and multivariate Gaussian generalized linear models were assessed while stratifying by sex and presence of anxiety and/or mood disorders. RESULTS: Our sample included 88 814 males and 106 717 females. For both sexes, perceiving oneself as overweight or underweight, regardless of actual weight status, was associated with lower life satisfaction (β = -0.93 to -0.30), compared to those who accurately perceived their weight as 'just about right'. Perceiving oneself as overweight or underweight was associated with more pronounced differences in life satisfaction scores in those with anxiety and/or mood disorders (β = -1.49 to -0.26) than in those without these disorders (β = -0.76 to -0.25). CONCLUSION: Weight perception is more indicative of life satisfaction than actual weight status, especially in those with anxiety and/or mood disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".