COVID-19 vaccine hesitancy and influence of professional medical guidance
Bibliographic record
Abstract
BACKGROUND: Vaccine hesitancy presents a major challenge during the COVID-19 pandemic. It is crucial to address the factors contributing to vaccine hesitancy necessary to control the associated morbidity and mortality. This study aimed to investigate the impact of professional medical guidance on the likelihood of receiving the COVID-19 vaccine in immigrants of USA and Canada. MATERIALS AND METHODS: A total of 92 immigrants in the USA and Canada who predominantly spoke Malayalam were recruited using social media platforms. An online survey was administered investigating participants' confidence in receiving the COVID-19 vaccine. Following, a short webinar was conducted by a medical professional explaining the efficacy and safety of the vaccine. A postwebinar survey was immediately given assessing the confidence and likelihood of receiving the vaccine. SPSS was used to generate descriptive statistics and Pearson Chi-square analysis where appropriate. RESULTS: < 0.01. CONCLUSION: Results from the current study demonstrate the successful delivery of professional medical guidance to the general public through online small-group sessions to help address the misconceptions surrounding the COVID-19 vaccine and combat vaccine hesitancy among vulnerable populations. Future studies should focus on interventions addressing vaccine hesitancy in larger and diverse populations and analyze other barriers to vaccination.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".