Correlates of Influenza Vaccine Uptake in Persons with Dementia in Canada
Bibliographic record
Abstract
As the Canadian population ages and rates of aging related disorders increase, it is important to find medical interventions that can promote health. Dementia is becoming an increasing concern among Canadians, with dementia comes the increased risk of infection and a greater chance of adverse health effects following infection. This makes it incredibly important to ensure that people with dementia are receiving a seasonal influenza vaccination. However, persons with dementia remain below the recommended rate of vaccination. This study examines how the presence of comorbidities may impact the rate of influenza vaccination among persons with dementia. Key comorbidities relating to dementia include COPD, heart disease, diabetes, and high blood pressure. As influenza vaccination for dementia patients is an incredibly important protective factor it is important to incorporate routine care that may increase vaccination rates. The presence of heart disease and COPD were both associated with a significantly higher vaccination rates. However, the relation to routine care was insignificant. These findings are important as it raises the question of why heart disease and COPD raised vaccination rates if not due to routine care. Continuing research targeting the dementia population is important to find ways to promote protective vaccination such as the seasonal flu shot.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".