COVID-19 VACCINE HESITANCY TRENDS AMONG CANADIAN HEALTHCARE WORKERS
Bibliographic record
Abstract
The novel coronavirus has been responsible for over 6 million deaths in the three years since the pandemic began. As vaccination efforts began in December of 2020, a wave of misinformation curtailed efforts to vaccine eligible adults across the globe, with healthcare workers not being immune to being influenced in this way. COVID-19 vaccine hesitancy among Canadian healthcare workers remains a poorly studied area. This systematic review aims to investigate what findings exists in the literature at present, and identify avenues for future research into strategies to combat this. A systematic literature review was conducted into existing publications using publicly available databases for article selection. Studies were systematically screened using the Covidence review platform and extracted data were analyzed in Microsoft Excel and International Business Machines’ Statistical Product and Service Solutions. Results identified a disparity between physician and nursing vaccine uptake rates as well as a dearth of information regarding physician assistant vaccine hesitancy. Male sex, age over 50 years old and employment in rehabilitation centres were all correlated with increased rates of vaccine acceptance. Potential areas of future research can help to highlight and test strategies to mitigate healthcare worker vaccine hesitancy which can be used to encourage improved uptake in future health crises.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".