Body weight misperception among Chinese international students in Canada \nduring the COVID-19 pandemic
Bibliographic record
Abstract
The phenomenon of BWM (body weight misperceptions) has been linked to a range of \nhealth risks. Unfortunately, the COVID-19 outbreak may have exacerbated this issue, leading to \ndetrimental weight fluctuations and an increased susceptibility to BWM. This study investigates \nBWM and its association with sociodemographic and lifestyle behaviors factors and self-perceived \nmental, physical, and overall health among Chinese international students in Canada during the \nsecond wave of the COVID-19 pandemic in early 2021. Data were collected from 296 eligible \nstudy participants through targeted sampling. Bivariate descriptive analyses and multivariate \nbinary logistic regression analyses (BLR) were used. \nThe study found that (29.1%) had overweight and (7.9%) had underweight misperceptions \namong Chinese international students in Canada. The study found that females had a higher \nlikelihood of reporting overweight misperceptions (OR=3.18, CI=1.39-7.24), while financial \ndissatisfaction and lifestyle behaviors such as watching television were associated with a higher \nrisk of overweight misperception (OR = 2.84, 95% CI = 1.39-5.82 and OR = 1.92, 95% CI = 1.09– \n3.34, respectively). On the other hand, exercise was associated with a lower risk of overweight \nmisperception (OR = 0.56, 95% CI = 0.32-0.98). This study also found that overweight \nmisperception have a lower likelihood of having poor overall health (OR = 0.61, 95% CI = 0.39- \n0.95), but no significant association with mental health (OR = 1.35, 95% CI = 0.86-2.11), or \nphysical health (OR = 1.15, 95% CI = 0.75-1.77). However, underweight misperception was \nassociated with a higher likelihood of poor overall health (OR = 1.54, 95% CI = 1.03-2.38), but \nno significant association was found with self-reported physical health (OR = 1.17, 95% CI = 0.78- \n1.76) and mental health (OR = 1.03, 95% CI = 0.67-1.56). \nIn conclusion, the study highlights that overweight misperceptions are prevalent among \nChinese international students in Canada, particularly among female and those who are financially \ndissatisfied and watch television. Exercise was found to lower the risk of overweight \nmisperception. Underweight misperception was associated with poor overall health. The study \nhighlights the need for targeted interventions to promote healthy lifestyles and well-being, and \nfurther research is required to identify additional factors and develop effective interventions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".