UNDERSTANDING OLDER ADULTS' FLU VACCINE HESITANCY: THE ROLE OF MEDICAL MISINFORMATION
Bibliographic record
Abstract
Abstract Influenza persists as a common communicable disease and remains a significant cause of disease burden across the world. Despite preventative therapies, such as influenza vaccination to reduce its spread and transmission, influenza continues to be a source of morbidity and mortality, even in developed countries. For the population over the age of 65, the effects of influenza virus may be more severe when they are compounded by pre-existing conditions and reduced natural immune function.In light of plateauing vaccination rates, a scoping review was conducted to map the literature and determine why seniors aged 65 and above refuse or fail to receive seasonal influenza vaccination. Nine peer-reviewed academic databases covering both social sciences and medical research were searched, along with the grey literature. A total of 6562 references were identified; after the screening process, 118 references were included in the final review. Thematic analysis focused on the broad areas that positively or negatively influence older adults’ decision-making regarding influenza vaccination, and this resulted in five main themes: (1) barriers to obtaining vaccination; (2) social factors; (3) personal characteristics; (4) individual subjectivity; and (5) direct clinical interventions.This review aims to identify gaps in knowledge and synthesize currently available information to make recommendations for future research, policy development and clinical practice. Increasing the vaccination rate among Canadian older adults will contribute to ongoing efforts to reduce the spread of the influenza virus among the population, reducing influenza-associated hospital admissions and deaths.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| 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".