Understanding COVID-19 Vaccine Education for Long-Term Care Workers: An Environmental Scan
Bibliographic record
Abstract
Aim: We sought to understand educational interventions delivered to long-term care home staff in Ontario, Canada, about COVID-19 vaccines. Background: Vaccinating staff in long-term care homes is critical to protecting workers and vulnerable residents from COVID-19. However, significant COVID-19 vaccine hesitancy was observed amongst healthcare workers globally when they were first introduced. While knowledge exists around why healthcare workers may express hesitancy towards vaccines, there remains an evidence gap on the delivery of educational interventions for promoting COVID-19 vaccine uptake in this population. Methods: We conducted an environmental scan consisting of 15 structured interviews with nurse practitioners and management in long-term care homes about education implemented to address staff vaccine hesitancy. We also extracted data from 3 relevant articles identified through a scoping review. Findings: One-to-one informal conversations were the primary method of delivering education, often supplemented with formal presentations and written information. Facilitators of the education were often peers, nurse practitioners, and directors of care. Equity, diversity, and inclusion (EDI) (e.g., providing education in multiple languages) were considered in some programs but rarely embedded in most formal delivery. The most common barrier to providing education was time constraints. Conclusions: This environmental scan highlights a range of educational initiatives that were introduced to boost vaccine confidence among workers in the long-term care sector during the COVID-19 pandemic. While there have been limited formal evaluations of these initiatives, there are informal lessons learned from these interventions that may be informative for the design of future vaccine education programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".