Vaccine acceptance, determinants, and attitudes toward vaccine among people experiencing homelessness: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background COVID-19 has caused millions of deaths globally, with vulnerable populations such as people experiencing homelessness (PEH) at higher risk. This systematic review and meta-analysis aims to identify the prevalence and key factors contributing to vaccine acceptance experienced by PEH. Methods The protocol of this study was registered in PROSPERO (CRD42023391659). We included studies that reported relevant information about vaccine acceptance or vaccine hesitant/refusal among PEH. Eight databases were systematically searched in January 2023. Meta-analysis was conducted for the prevalence of vaccine acceptance, vaccine uptake, and factors associated with vaccine acceptance. Attitudes toward vaccines were combined into bar charts. Result A total of 29 papers were included in this systematic review and 19 papers were included for meta-analysis. The pooled prevalence of COVID-19 vaccine acceptance among PEH was 66% (95%CI: 58%-73%). Our meta-regression showed vaccine acceptance was significantly increased over time. Moreover, subgroup meta-analysis showed that PEH were more likely to accept the COVID-19 vaccine after June 2021 (78%, 95%CI: 65%-86%) compared with earlier period (56%, 95%CI: 54%-59%). Subgroup meta-analysis also revealed that women and participants without underlying medical condition (chronic diseases) were significantly less likely to accept the COVID-19 vaccine, compared to men and those with medical conditions, respectively. Conclusion The study emphasizes the need for targeted public health interventions aimed at increasing vaccine acceptance among PEH, especially at the early stage of the pandemic, among females, those without underlying medical conditions, being Black (in Canada and the USA), and young people. These interventions should address the common concerns of vaccine safety, adverse effects, effectiveness, and distrust in health care systems. In addition to offering vaccinations in different areas convenient to them, education programs could be established to increase vaccine acceptance among PEH.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".