Consequences of COVID-19 Vaccine Hesitancy Among Healthcare Providers During the First 10 Months of Vaccine Availability: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Throughout the COVID-19 pandemic, healthcare providers (HCPs)-including nurses-have played important roles in the vaccination effort. It is expected that COVID-19 vaccine hesitancy among HCPs has numerous consequences; however, the scope of these consequences and their impacts on providers, patients, and the broader healthcare system remained unclear. PURPOSE: To identify existing and emerging evidence to understand the state of knowledge of the consequences of COVID-19 vaccine hesitancy among HCPs. METHODS: A scoping review was completed based upon the JBI scoping review methodology. The databases searched included OVID Medline, EBSCOhost CINAHL, ProQuest Nursing and Allied Health Source, ProQuest APA PsycInfo, and ProQuest Dissertations and Theses. The final literature search was completed on June 2, 2022. Studies were screened and retrieved based on predefined inclusion and exclusion criteria using Covidence reference management software. Data extraction followed criteria recommended in the JBI scoping review framework with additional relevant variables identified by the authors. RESULTS: A total of 33 sources were included in the review. Consequences of HCP COVID-19 vaccine hesitancy were grouped under three themes and seven subthemes. Consequences affecting HCPs included health-related, psychosocial, and employment-related consequences. Consequences affecting patients pertained to COVID-19 vaccination communication and COVID-19 vaccination practices of HCPs. Consequences to the healthcare system involved consequences to coworkers and employment/attendance/staffing-related consequences. CONCLUSIONS: Healthcare provider COVID-19 vaccine hesitancy was found to have numerous consequences. By understanding the scope and extent of these consequences, healthcare leaders, researchers, and HCPs can work together to protect providers, patients, and healthcare systems.
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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 | 0.015 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".