COVID-19 Vaccine Hesitancy Among Health Care Workers in Thailand: The Comparative Results of Two Cross-Sectional Online Surveys Before and After Vaccine Availability
Bibliographic record
Abstract
Introduction: The arrival of COVID-19 vaccines in Thailand has supported the fight against the COVID-19 pandemic. This study examined COVID-19 vaccine acceptance among health care workers (HCWs) in Thailand before and after vaccines' availability and investigated factors (both enablers and barriers) affecting their decisions. Methods: Two online self-administered questionnaires were distributed to HCWs in two time-periods: (1) the pre-vaccine arrival period (prior to COVID-19 vaccines' arrival in Thailand, January 28 to February 16, 2021); and (2) the post-vaccine arrival period (April 21 to May 9, 2021). Descriptive analyses and multinomial logistic regression were conducted to examine factors associated with vaccine hesitancy. Results: There were 55,068 respondents in the pre-vaccine arrival period and 27,319 respondents in the post-vaccine arrival period. In the pre-vaccine arrival period, 55.0% of respondents were willing to accept the vaccines, 35.4% were uncertain, and 9.6% declined. In the post-vaccine arrival period, ~16% already received two doses of either the Sinovac or AstraZeneca vaccine, and 43% were administered one dose. Approximately 12% of those who had received the first dose were uncertain or not willing to accept the second dose. Demographic and socio-demographic factors of participants, including their sex, place of residence, and whether they were frontline COVID-19 workers, were found to be the significant factors explaining vaccination hesitancy. Moreover, when comparing the pre-vaccine arrival and post-vaccine arrival periods, it was found that older HCWs were more likely to decline a COVID-19 vaccine in the pre-vaccine arrival period; on the other hand, older HCWs were less likely to decline or be uncertain to receive a COVID-19 vaccine in the post-vaccine arrival period. Conclusion: Information on HCWs' acceptance of COVID-19 vaccines, including who is more likely to accept the vaccines, could assist in planning vaccine allocation to both HCWs and the general public, who often believe HCWs' recommendations. This study's findings set out how policies can be addressed to reduce vaccine hesitancy. This study also highlights HCWs' characteristics (including gender, work region, occupation, and history of receiving influenza vaccination) and the reasons they cited for their vaccine acceptance or hesitance.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".