Possible predictors of Covid-19 vaccine hesitancy in the psychiatric population – A scoping review
Bibliographic record
Abstract
Background: The Covid-19 pandemic brought vaccination to the front of the series of measures implemented to address the chain-reaction outbreaks that continue to cause loss and suffering. In spite of its proven efficacy, a considerable percentage of the population remains hesitant or right-out opposed. A need for informing public health strategies not only in regards to the current pandemic but for future similar developments remains of utmost importance for researchers and clinicians alike, especially when it comes to vulnerable categories of population. Identifying risk factors associated with vaccine hesitancy in the psychiatric population is the aim of this scoping review. Methods: We performed a systematic search on the topic of Covid-19 vaccine hesitancy in relation to psychiatric disorders, using three databases: Medline, PubMed and Embase. Inclusion criteria focused on studies looking at individuals with a psychiatric disorder in the context of the Covid-19 vaccine hesitancy where possible determinant factors were discussed. Results: Fifteen articles out of 219 publications on the topic of Covid 19 vaccine hesitancy met our inclusion criteria for this review. The common findings of these studies recognize the following risk factors for Covid 19 hesitancy: diagnosis of severe mental illness such as schizophrenia, lower socioeconomic status, lower educational level, and young age. Conclusions: Our findings may contribute to the proactive development of educational strategies targeting the psychiatric population in the context of cultural, ethnic, age and gender diversity, in order to safeguard the wellbeing of all when facing pandemic events. Overarching future directions include creating vaccination promotion strategies specific for the psychiatric 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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".