Protecting individuals living with overweight and obesity: Attitudes and concerns toward COVID‐19 vaccination in Canada
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess attitudes toward coronavirus disease 2019 (COVID-19) vaccination and the degree of fear of COVID-19 among those living with obesity. METHODS: Two samples were recruited for an online survey: one sample was a representative sample of Canadians living with overweight and obesity (n = 1,089), and the second was a convenience sample of individuals recruited from obesity clinical services or patient organizations (n = 980). Respondents completed ratings of their comfort receiving a COVID-19 vaccination along with the Vaccine Hesitancy Scale and the Fear of COVID-19 Scale. RESULTS: Approximately one-third of respondents, regardless of sample or weight category, were not comfortable receiving a vaccination, and one-half expressed moderate or greater perceived risks of vaccination. Confidence in vaccinations was extremely low, especially for those in the clinical sample. Fear of COVID-19 was substantial and predicted attitudes toward vaccination. Females were less comfortable receiving the vaccine and perceived more risks than males. CONCLUSIONS: These results suggest those living with obesity are highly ambivalent about COVID-19 vaccination. Despite their being at high risk, their confidence in vaccines is very low. Results suggest the need for patient-centered counseling, with a focus on shared decision-making to strengthen confidence and reduce perceived risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".