Exploring the uptake of COVID-19 vaccination amongst respiratory therapists in Canada
Bibliographic record
Abstract
Introduction/background COVID-19 vaccination uptake rates and responses by Canadian respiratory therapists (RTs) were investigated along with factors that may be shown to play a role in vaccination hesitancy. Methods An anonymous survey using SurveyMonkey® on vaccination uptake rates, responses and attitudes was made available to student RTs, graduate RTs and registered RTs in Canada from July to October of 2021. Pearson's chi-square tests were performed to evaluate association between vaccination status and the other categorical parameters evaluated. Results A total of 1013 surveys (8.0% of target population) were completed fully and included in the data analysis. Of the surveyed RT population, 90.42% received their vaccination as soon as it was made available compared to Canada's Ministry of Health's published rate at the time of 86.27% for all Canadian healthcare workers. There was a significant (p = 0.013) association between early vaccination and age and a significant (p = 0.036) association between vaccination status and a participant's response on whether or not they have a family member or know someone who has had COVID-19. There was also a significant (p \< 0.001) association between vaccination status and attitudes towards trusting science to develop safe, effective, new vaccines and trusting the Ministry of Health to ensure that vaccines are safe. There was no significant association between vaccination status and gender, province/territory of residency/work, level of education and level of involvement with COVID-19 patients. Conclusion The results suggest that RT groups across Canada had higher early vaccination uptake rates than general healthcare worker groups and that age, relationship to people with COVID-19 and trust in science played a significant role in their vaccination uptake rates.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".