Identifying COVID-19 and H1N1 vaccination hesitancy or refusal among health care providers across North America, the United Kingdom, Europe, and Australia: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The aim of this scoping review is to describe and map the evidence on COVID-19 and H1N1 vaccination hesitancy or refusal among physicians, nurses, and pharmacists across North America, the United Kingdom, Europe, and Australia. INTRODUCTION: When global pandemics occur, including the coronavirus (COVID-19) pandemic, which originated in 2020, and the swine flu influenza pandemic (H1N1) of 2009, there is increased pressure for pharmaceutical companies and government agencies to develop safe and effective vaccines against these highly contagious illnesses. Following development and approvals, it then becomes essential that priority populations, including frontline health care providers, opt to receive these vaccinations to prevent illness and potential transmission to their patients. However, vaccine hesitancy or refusal has played a significant role in suboptimal vaccination rates globally. As health care providers, including physicians, nurses, and pharmacists, often administer vaccines, their vaccination views and behaviors are of great importance because they can directly affect the vaccination decisions of their patients. INCLUSION CRITERIA: The review will identify factors affecting COVID-19 and H1N1 vaccine hesitancy or refusal among physicians, nurses, and pharmacists across a range of countries. Published and unpublished evidence, including quantitative, qualitative, mixed methods research, and gray literature, will be eligible for inclusion. METHODS: This scoping review protocol will follow JBI methodology. The search strategy will be developed with support from a health sciences librarian scientist to identify relevant evidence. Screening and data extraction will be conducted by two reviewers, with findings summarized and presented through narrative descriptions, tables, and figures.
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 | 0.105 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".