Understanding attitudes and beliefs regarding COVID-19 vaccines among transitional-aged youth with mental health concerns: a youth-led qualitative study
Bibliographic record
Abstract
BACKGROUND: Transitional-aged youth (16-29 years) with mental health concerns have experienced a disproportionate burden of the COVID-19 pandemic. Vaccination is limited in this population; however, determinants of its vaccine hesitancy are not yet thoroughly characterised. OBJECTIVES: This study aimed to answer the following research question: What are the beliefs and attitudes of youth with mental illness about COVID-19 vaccines, and how do these perspectives affect vaccine acceptance? The study aims to generate findings to inform the development of vaccine resources specific to youth with mental health concerns. METHODS: A qualitative methodology with a youth engagement focus was used to conduct in-depth semistructured interviews with transitional-aged youth aged 16-29 years with one or more self-reported mental health diagnoses or concerns. Mental health concerns encompassed a wide range of symptoms and diagnoses, including mood disorders, anxiety disorders, neurodevelopmental disorders and personality disorders. Participants were recruited from seven main mental health clinical and support networks across Canada. Transcripts from 46 youth and 6 family member interviews were analysed using thematic analysis. RESULTS: Two major themes were generated: (1) factors affecting trust in COVID-19 vaccines and (2) mental health influences and safety considerations in vaccine decision-making. Subthemes included trust in vaccines, trust in healthcare providers, trust in government and mistreatment towards racialised populations, and direct and indirect influences of mental health. CONCLUSIONS: Our analysis suggests how lived experiences of mental illness affected vaccine decision-making and related factors that can be targeted to increase vaccine uptake. Our findings provide new insights into vaccine attitudes among youth with mental health concerns, which is highly relevant to ongoing vaccination efforts for new COVID-19 strains as well as other transmissible diseases and future pandemics. Next steps include cocreating youth-specific public health and clinical resources to encourage vaccination in this population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".