Older adolescents and young adults willingness to receive the COVID-19 vaccine: Implications for informing public health strategies
Bibliographic record
Abstract
IMPORTANCE: The success in ending the COVID-19 pandemic rests partly on the mass uptake of the COVID-19 vaccine. Little work has been done to understand vaccine willingness among older adolescents and young adults. This is important since this age group may be less likely to adhere to public health guidelines. OBJECTIVE: To understand willingness of getting a vaccine and reasons for vaccine hesitancy among a sample of older adolescents and young adults. DESIGN: Data were from the Well-Being and Experiences study (The WE Study), a longitudinal community-based sample of older adolescents and young adults collected from Winnipeg, Manitoba, Canada from 2017 to 2020 (n = 664). SETTING: The study setting was a community-based observational longitudinal study. PARTICIPANTS: Participants for the study were aged 14 to 17 years old at baseline in 2016-17 (n = 1000). Data were also collected on one parent/caregiver. Waves 2 (n = 747) and 3 (n = 664) were collected in 2019 and 2020, respectively. EXPOSURES: The main exposures were sociodemographic factors, health conditions, COVID-19 knowledge, and adversity history. MAIN OUTCOMES: The main outcomes were COVID-19 vaccine willingness, hesitancy, and reasons for hesitancy. RESULTS: Willingness to get a COVID-19 vaccine was 65.4%. Willingness did not differ by age, sex, or mental health conditions, but did differ for other sociodemographic characteristics, physical health conditions, COVID-19 knowledge, practicing social/physical distancing, and adversity history. The most common reasons for not wanting a vaccine were related to safety, knowledge, and effectiveness. Sex differences were noted. CONCLUSIONS AND RELEVANCE: Increasing uptake of the COVID-19 vaccine among older adolescents and young adults may rely on targeting individuals from households with lower income, financial burden, and adversity history, and generating public health messaging specifically aimed at vaccine safety, how it works to protect against illness, and why it is important to protect oneself against a COVID-19 infection.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".