The Effects of Obesity on COVID-19 Vaccination Rates and Efficacy
Bibliographic record
Abstract
Obesity rates and COVID-19 vaccination rates among Canadians are increasing, but whether they are correlated has not been studied (Statistics Canada). Additionally, the potential correlation between obesity and COVID-19 vaccine efficacy has not yet been formally concluded (Kipshidze et al.). Determining how obesity affects vaccination is necessary to assure that the obese population is fully and effectively immunized against the virus. Three important factors, mortality, infection, and vaccination rates, were compared across all Canadian provinces, testing the vaccine efficacy and hesitancy in areas with different obesity populations (Government of Canada). Linear regressions were performed to test for correlations between the vaccination factors and obesity. A Mann-Whitney U test was used to compare provinces with obesity percentages less than 24% against those greater than 24%. The results indicate a positive correlation between obesity and vaccination rates, as well as a significant difference in all factors between the low and high obese percentage populations. It can be concluded that the obese population does not require extra attention and is more receptive than average in terms of COVD-19 vaccination and protection.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".