Examining COVID-19 vaccine uptake and attitudes among 2SLGBTQ+ youth experiencing homelessness
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has disproportionately impacted 2SLGBTQ+ youth experiencing homelessness. Little is known about vaccine attitudes and uptake among this population. To address this, the objectives of this study were to explore this group's COVID-19 vaccine attitudes, and facilitators and barriers impacting vaccine uptake. METHODS: 2SLGBTQ+ youth experiencing homelessness in the Greater Toronto Area were recruited to participate in online surveys assessing demographic characteristics, mental health, health service use, and COVID-19 vaccine attitudes. Descriptive statistics and statistical tests were used to analyze survey data to explore variables associated with vaccine confidence. Additionally, a select group of youth and frontline workers from youth serving organizations were invited to participate in online one-on-one interviews. An iterative thematic content approach was used to analyze interview data. Quantitative and qualitative data were merged for interpretation by use of a convergent parallel analytical design. RESULTS: Ninety-two youth completed surveys and 32 youth and 15 key informants participated in one-on-one interviews. Quantitative and qualitative data showed that the majority of 2SLGBTQ+ youth experiencing homelessness were confident in the COVID-19 vaccine; however, numerous youth were non-vaccine confident due to mistrust in the healthcare system, lack of targeted vaccine-related public health information, concerns about safety and side effects, and accessibility issues. Solutions to increase vaccine confidence were provided, including fostering trust, targeted public health messaging, and addressing accessibility needs. CONCLUSION: Our study highlights the need for the vaccine strategy and rollouts to prioritize 2SLGBTQ+ youth experiencing homelessness and to address the pervasive health disparities that have been exacerbated by the pandemic.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".