O-366 Vaccine Hesitancy among Canadian Paramedics during the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction Paramedics may be at an increased risk of interacting with COVID-19-positive individuals, making understanding the factors that influence paramedics’ vaccination decisions increasingly important. Objectives We aim to investigate factors that may influence paramedics’ likelihood of COVID-19 vaccination. Methods Canadian paramedics from five provinces (Alberta, British Columbia, Manitoba, Ontario, Saskatchewan) working during the COVID-19 pandemic were voluntarily recruited through posters, social media, and emails from collaborating paramedic organizations. Participants completed online questionnaires between January and May of 2021 that assessed COVID-19 vaccine status, vaccine hesitancy, and intent to be vaccinated. Differences in proportions tests were used to compare agreement scores, calculated by combining proportions of participants who responded ‘strongly agree’ and ‘agree’ to questionnaire items. Results Of the 2178 paramedics recruited, 95.7% completed the questionnaire (76.6% vaccinated). While most participants (89.4%) agreed that people should be vaccinated against COVID-19 and that vaccinations are necessary (94.7%), fewer participants agreed that COVID-19 vaccines are safe (78.5%) as compared to routine vaccines (86.1%, p<0.001), such as influenza vaccinations. However, vaccinated participants were more likely than unvaccinated participants to agree that routine vaccines are safe (90.5% vs. 76.2%, p<0.001) and that COVID-19 vaccines are safe (87.3% vs. 52.4%, p<0.001). Unvaccinated participants were more likely than vaccinated participants to report no intention of being vaccinated (14.2% vs. 0.1%, p<0.001), to report that they would get vaccinated but would wait (22.5% vs. 9.4%, p<0.001), and to report competing priorities were preventing them from getting vaccinated (9.0% vs. 2.4%, p<0.001). Vaccinated participants were more likely to report that they would get a COVID-19 vaccine if recommended by public health experts (90.5% vs. 55.9%, p<0.001). Conclusion Most paramedics believe COVID-19 vaccines to be safe and necessary. However, a sizeable proportion of paramedics reported no intention of getting vaccinated. Further analyses are needed to determine which factors influence their vaccination decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".