PROTOCOL: Exploring education to support vaccine confidence amongst healthcare and long‐term care staff amidst the COVID‐19 pandemic: A protocol for a living scoping review
Bibliographic record
Abstract
Despite the demonstrated effectiveness of vaccines, varying levels of hesitancy were observed among healthcare and long-term care workers, who were prioritized in the roll out of COVID-19 vaccines due to their high risk of exposure to SARS-CoV-2 transmission. However, the evidence around the measurable impact of various educational interventions to improve vaccine confidence is limited. The proposed scoping review is intended to explore any emerging research and experiences of delivering educational interventions to improve COVID-19 vaccine confidence among health and long-term care workforces. We aim to identify characteristics of both informal and formal educational interventions delivered during the pandemic to support COVID-19 vaccine hesitancy. Using the guidance outlined by the Joanna Briggs Institute, we intend to search five databases including, Ovid MEDLINE and Web of Science, as well as grey literature. We will consider all study designs and reports in an effort to include a breadth of sources to ensure our review will capture preliminary evidence, as well as more exploratory experiences of COVID-19 vaccine education delivery. Articles will be screened by three reviewers independently and the data will be charted, and results described narratively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.140 | 0.030 |
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".