COVID-19 Vaccine Uptake among People with Spinal Cord Injury and Dysfunction in Ontario, Canada: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
Persons with disabilities experience numerous barriers to healthcare access including vaccine accessibility. The purpose of this study was to determine COVID-19 vaccine uptake in the spinal cord injury and disease (SCI/D) population of Ontario and identify potential factors influencing C OVID-19 vaccine uptake. This was a retrospective closed-cohort study using administrative health data on individuals with SCI/D of traumatic and non-traumatic causes to examine the monthly number of COVID-19 vaccine doses received between December 2020 and December 2023. Logistic regression analysis was used to examine the potential association between socio-demographic, clinical, and neighbourhood characteristics with initial COVID-19 vaccine receipt and booster dose uptake. By the end of the observation period in December 2023, 82.9% received the full two-dose coverage and 65.6% received at least one additional booster dose in a cohort of 3574 individuals with SCI/D. SCI/D individuals showed a comparable COVID-19 vaccine uptake percentage to the general population. Sociodemographic, clinical, and neighbourhood characteristics were associated with COVID-19 vaccine uptake in the SCI/D population, including age, type of injury, number of comorbidities, mental health history, and neighbourhood characteristics such as income. Further investigation is necessary to determine the causation effects of these relationships with vaccine uptake to address health equity concerns.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".