Vaccination, time lost from work, and COVID-19 infections: a Canadian healthcare worker retrospective cohort study
Bibliographic record
Abstract
The COVID-19 pandemic highlighted hurdles for healthcare delivery and personnel globally. Vaccination has been an important tool for preventing severe illness and death in healthcare workers (HCWs) as well as the public at large. However, vaccination has resulted in some HCWs requiring time off work post-vaccination to recover from adverse events. We aimed to understand which HCWs needed to take time off work post-vaccination, for which vaccine types and sequence, and how post-vaccination absence impacted uptake of booster doses in a cohort of 26,267 Canadian HCWs. By March 31, 2022, more than 98% had received at least two doses of the approved COVID-19 vaccines, following a two-dose mandate. We found that recent vaccination and longer intervals between doses were associated with significantly higher odds of time-loss, whereas being a medical resident and receiving the BNT162b2 vaccine were associated with lower odds. A history of lab-confirmed SARS-CoV-2 infection was associated with lower odds of receiving a booster dose compared with no documented infection, aOR 0.61 (95% CI: 0.55, 0.68). Similarly, taking sick time following the first or second dose was associated with lower odds of receiving a booster dose, aOR 0.83 (95% CI: 0.75, 0.90). As SARS-CoV-2 becomes endemic, the number and timing of additional doses for HCWs requires consideration of prevention of illness as well as service disruption from post-vaccination time-loss. Care should be taken to ensure adequate staffing if many HCWs are being vaccinated, especially for coverage for those who are more likely to need time off to recover.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".