University students’ perspectives, planned uptake, and hesitancy regarding the COVID-19 vaccine: A multi-methods study
Bibliographic record
Abstract
PURPOSE: To investigate university students' willingness to receive a COVID-19 vaccine when it becomes available to them. METHOD: A multi-methods approach was used-online convenience sample surveys and semi-structured interviews-of young adults attending a large Canadian public university. Two survey samples were collected (June 20-July 28, 2020 and September 22-October 17, 2020). Semi-structured interviews were conducted following each survey, interviewing 20 students in each round. RESULTS: In June 77.8% of surveyed students (n = 483) were willing to get the COVID-19 vaccine; in September 79.6% were willing (n = 1269). Multinomial and binary logistic regression analyses found that increasing perception of the severity of COVID-19 predicted the likelihood that a respondent was willing to get the COVID-19 vaccine in both surveys. In the latter survey students who indicated they would be encouraged to get the COVID-19 vaccine if their doctor/pharmacist recommended it were 76 times more likely to be willing to get the vaccine than those who would not be encouraged by medical advice. Interviews revealed concerns about the speed of the vaccine roll out, safety, and efficacy. CONCLUSIONS: The majority of university students intend to get the COVID-19 vaccine, but there are nuanced concerns about efficacy and safety that must be taken into account by public health authorities as the vaccine becomes available to this group. Ensuring that family doctors, pharmacists, and other front-line healthcare workers have consistent and clear information regarding the benefits of vaccination will be critical to encouraging uptake among young adults.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".