Intentions of public school teachers in British Columbia, Canada to receive a COVID-19 vaccine
Bibliographic record
Abstract
BACKGROUND: To control the COVID-19 pandemic high vaccine acceptability and uptake will be needed. Teachers represent a priority population to minimize social disruption and ensure continuity in education, which is vital for the well-being and healthy development of youth during the pandemic. The objective of this analysis was to measure public school teachers' intentions to receive a COVID-19 vaccine in British Columbia (BC), Canada. METHODS: A population-wide cross-sectional online survey from August to November 2020 asked all BC public school teachers with an available email address how likely they were to receive a COVID-19 vaccine. Two multivariable logistic regression models explored separately sociodemographic and vaccine hesitancy predictors for intention to receive a COVID-19 vaccine. RESULTS: A total of 5,076 teachers participated. The majority, 89.7%, reported they were likely or very likely to accept a COVID-19 vaccine. In multivariable regression, sociodemographic predictors of intention to be vaccinated included being male, having an educational background in science or engineering, and using reliable information sources on vaccination such as public health and health care providers. Teachers who reported lower levels of vaccine hesitancy, higher general vaccine knowledge, and belief that COVID-19 was a serious illness were more likely to intend to receive a COVID-19 vaccine. CONCLUSION: A high proportion of public-school teachers in BC intend to receive a COVID-19 vaccine. Continued monitoring of vaccine intentions will be important to inform public health vaccine implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".