Correlates of Weight‐Loss Methods Among Young Adults in Canada
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the prevalence of various weight-loss behaviors among young adults in Canada and differences in the use of these methods by demographic characteristics, health literacy, and perceived body size. METHODS: Data from the 2016 wave of the Canada Food Study were used, which collected self-reported information from 3,000 young adults in five cities. Linear regression models were conducted to investigate correlates of the number and type of weight-loss methods used across the following four categories: dietary changes, physical activity, assisted weight-loss methods, and unhealthy behaviors. RESULTS: In the past 12 months, more than half of respondents reported a weight-loss attempt, and nearly one-fifth engaged in an unhealthy weight-loss method. The risk of engaging in a greater number of weight-loss behaviors across categories was higher for women, nonbinary-gendered individuals, and individuals who perceived themselves as having overweight or obesity. Respondents with lower health literacy engaged in a significantly greater number of unhealthy methods. CONCLUSIONS: Many young adults use healthier weight-loss strategies, but a concerning number use multiple and/or unhealthy weight-loss methods as well. Furthermore, there are subgroup differences in weight-loss method engagement, which holds significance for public health efforts aiming to improve weight-related behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| 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".