Factors affecting COVID-19 vaccine hesitancy among healthcare providers in 23 countries
Bibliographic record
Abstract
BACKGROUND: Several early COVID-19 studies aimed to assess the potential acceptance of a vaccine among healthcare providers, but relatively few studies of this population have been published since the vaccines became widely available. Vaccine safety, speed of development, and low perceived disease risk were commonly cited as factors for COVID-19 vaccine hesitancy among this group. PURPOSE AND METHODS: In a secondary analysis based on a cross-sectional, structured survey, the authors aimed to assess the associations between self-reported vaccine hesitancy and a number of sociodemographic and COVID-19 vaccine perception factors using data from 3,295 healthcare providers (physicians, nurses, community health workers, other healthcare providers) in 23 countries. FINDINGS: 494 (15.0%) of the participants reported vaccine hesitancy, of whom 132 (4.0%) would outright refuse to accept a COVID-19 vaccine. Physicians were the least hesitant. Vaccine hesitancy was more likely to occur among those with less than the median income and, to a lesser degree, younger age. Safety and risk concerns and lack of trust that vaccines would be equitably distributed were strongly associated with hesitancy, less so were concerns about the efficacy of COVID-19 vaccines. INTERPRETATION: Findings suggest a need to address safety and risk concerns through tailored messaging, training, and/or incentive approaches among healthcare providers, as well as the need for international and national vaccination efforts to ensure equitable distribution.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".