Sentiment analysis of employees and COVID-19 vaccine hesitancy at workplace
Bibliographic record
Abstract
Abstract Vaccination is a potent means to combat the spread of infectious disease epidemics or pandemics, such as the COVID-19 pandemic. However, getting sufficient people to accept the vaccine and achieve herd immunity remains a significant challenge. This study evaluates preschool workers’ sentiments and challenges during in-person schooling amidst the COVID-19 pandemic and attitudes toward COVID-19 vaccination through a survey. The study surveyed preschool workers as part of a consulting project. Workers’ sentiments are analyzed using Azure Machine Learning (AML) data analytic method, statistical analysis, and theoretical evaluation. The results show that no association exists between employee job category (teachers versus support staff) and COVID-19 vaccine hesitancy. However, fewer teaching staff were hesitant to take the COVID-19 vaccine than the support staff (46% < 50%), but the difference was not significant [Chi-Square (χ2) = 0.009; p>0.05]. The overall sentiments of all preschool workers showed 46%, indicating low neutrality, implying hesitancy toward the COVID-19 vaccine. Preschool workers who felt optimistic about the vaccine’s potency cited scientific reasons; those with neutral sentiments needed further information and assurances about the side effects, indicating that appropriate health education can sway more people positively towards accepting the vaccine; people with negative sentiments displayed distrust, fear, personal beliefs, and misinformation. The results also highlight the need to educate all workers on vaccine potency despite the levels of education. Regardless of academic qualifications, any uninformed person can fall prey to misinformation and conspiracy theories. Proper health education helps people make informed decisions.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".