Temporal trends in COVID-19 vaccine uptake among social housing residents compared to the general population in Ontario, Canada: A population-based panel study
Bibliographic record
Abstract
BACKGROUND: This study examined temporal trends in COVID-19 vaccine uptake among social housing residents compared to the general population in Ontario, Canada, during the first year of vaccine availability. METHODS: We analyzed 2021 COVID-19 vaccination data from Ontario administrative databases. The social housing population was identified using postal codes of designated social housing buildings. Vaccination rates were compared quarterly across age and sex categories between social housing residents and the general population. RESULTS: In 2021, there were 14,842,488 eligible individuals identified in Ontario administrative health data, with 328,276 individuals residing in social housing. By the end of 2021, 75.45 % of adult social housing residents were fully vaccinated (2 or more COVID-19 vaccine doses) compared to 87.46 % of the general adult population. This gap persisted over time and across sexes. Over the same period, 30.61 % of the children and youth in social housing achieved full vaccination rates compared to 30.21 % of the general population, with greater vaccine uptake among females. CONCLUSION: Despite COVID-19 vaccination policies aimed at prioritizing vulnerable groups in Ontario, Canada, adult social housing residents had lower vaccination rates compared to the general population. Children and youth in social housing achieved slightly higher vaccination coverage. These findings underscore the need for more targeted efforts to improve vaccine accessibility and uptake among social housing residents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".