Relationship between mental health and substance abuse on COVID-19 vaccine hesitancy in youth: A mixed methods longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Mental health and substance use challenges are highly correlated in youth and have been speculated to be associated with COVID-19 vaccine hesitancy. Literature has also suggested that mental health challenges in youth have increased during the COVID-19 pandemic. However, the longitudinal relationship between mental health challenges in youth and COVID-19 vaccine hesitancy is not well established. OBJECTIVE: We examined the relationship between mental health, substance use and COVID-19 vaccine hesitancy in youth during the COVID-19 pandemic. METHODS: Youth ages 14 to 29-years participated in a longitudinal survey study. Participants provided sociodemographic, mental health, and substance use data, as well as qualitative and quantitative information on their vaccine perspectives every two months between February 2021 to August 2021, and on February 2022. Generalized estimating equation logistic regression models were used to analyze the effect of mental health and substance use on vaccine hesitancy over time. Qualitative content area analyses were used to identify trends in vaccine attitudes. RESULTS: Mental health challenges and substance use frequency were associated with vaccine hesitancy, and significantly increased the odds of vaccine hesitancy over time. Additionally, mental health challenges were associated with decreases in vaccine hesitancy (OR: 0.80 (95% CI 0.66, 0.97)) when vaccines first began to emerge, but increases in vaccine hesitancy (OR: 1.72 (95% CI 1.32, 2.26)) one year later. Participants reported perceptions regarding vaccine safety and efficacy were the primary determinants influencing hesitant, uncertain, and acceptant vaccine attitudes. Additionally, changes in vaccine attitudes over time for some participants, were associated with changes in mental health. CONCLUSIONS: Increases in mental health challenges and substance use were associated with increases in COVID-19 vaccine hesitancy in youth over the COVID-19 pandemic. Health policy agencies should be aware of the potential impact of mental health challenges and substance use in youth, when developing vaccine policy and programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".