Characterizing intentions to receive the COVID-19 vaccine among the general population in British Columbia based on their future intentions towards the seasonal influenza vaccine
Bibliographic record
Abstract
Introduction: This study assessed the relationship between intention to receive a COVID-19 vaccine and intention to receive a seasonal influenza vaccine, as well as how intention to receive a COVID-19 vaccine has changed during the pandemic. Methods: Residents of British Columbia aged 25-69 years were invited to complete an online cross-sectional survey from August 2020 - March 2021. Mixed-effects logistic regression models assessed the relationship between intention to receive a COVID-19 vaccine and intention to receive the seasonal influenza vaccine. A generalized additive mixed model was used to investigate changes in COVID-19 vaccine intention during the pandemic (August 2020-March 2021). The relationship between intention to receive a COVID-19 vaccine and retrospective overall perceived value of vaccines prior to and during the pandemic was also considered. Results: Of 6,333 participants, 80.2 % of participants were 'somewhat or very likely' to receive a COVID-19 vaccine and 69 % of participants reported intending to receive a seasonal influenza vaccine. In multivariable modeling, intention to receive a COVID-19 vaccine was strongly associated with intention to receive a seasonal influenza vaccine (aOR = 4.25, 95 %CI 3.33-5.43). Intention to receive a COVID-19 vaccine increased over the study period (p < 0.0001), with the largest increase coinciding with the announcement of forthcoming approvals of COVID-19 vaccines in November 2020 (aOR = 1.45, 95 %CI 1.11-1.91). Conclusion: Intention to receive the COVID-19 vaccine was associated seasonal influenza vaccine intention, which is an important relationship to measure for implementation and future planning of COVID-19 booster doses. We found an increase in the intention to receive a COVID-19 vaccine after public announcements of forthcoming vaccine approval, which highlights the importance of ongoing monitoring and reporting of vaccine uptake, and the potential impact of emerging vaccine safety and efficacy information may have on vaccine acceptance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".