COVID-19 vaccine uptake among healthcare workers: an achievable quality improvement target
Bibliographic record
Abstract
There is a need to optimize SARS-CoV-2 vaccination rates amongst healthcare workers (HCWs) to protect staff and patients from healthcare-associated COVID-19 infection. During the COVID-19 pandemic, many organizations implemented vaccine mandates for HCWs. Whether or not a traditional quality improvement approach can achieve high-rates of COVID-19 vaccination is not known. Our organization undertook iterative changes that focused on the barriers to vaccine uptake. These barriers were identified through huddles, and addressed through extensive peer outreach, with a focus on access and issues related to equity, diversity and inclusion. The outreach interventions were informed by real-time data on COVID-19 vaccine uptake in our organization. The vaccine rate reached 92.3% by 6 December 2021 with minimal differences in vaccine uptake by professional role, clinical department, facility or whether the staff had a patient facing role. Improving vaccine uptake should be a quality improvement target in healthcare organizations and our experience shows that high vaccine rates are achievable through concerted efforts targeting specific barriers to vaccine confidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 teacher head, 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".