Public health unit funding per capita and seasonal influenza vaccination among youth and adults in Ontario, Canada in 2013/2014 and 2018/2019
Bibliographic record
Abstract
BACKGROUND: Influenza vaccines are crucial in reducing the risk of influenza symptoms. We aimed to: (1) estimate the association between public health unit (PHU) funding per capita and influenza vaccination among individuals aged 12 and older in Ontario in 2013/2014 and 2018/2019 and (2) determine whether any observed associations were heterogeneous across household income groups, gender and age categories. METHODS: Cross-sectional studies were conducted using the Canadian Community Health Survey, a population-representative survey that collects annual health data. PHU funding per capita was measured using the approved provincial funding for mandatory programmes and the Canadian Census Population Estimates. Self-reported influenza vaccination status in the past year was used. Multilevel logistic regression was used to estimate the association. RESULTS: A case-complete weighted dataset revealed that 33.2% in 2013/2014 and 35.1% in 2018/2019 of respondents were vaccinated. In 2013/2014, every standard deviation (SD) increase in PHU funding per capita was associated with vaccination (OR: 1.08; 95% CI: 1.01, 1.15; SD: 14.1). Furthermore, for every SD increase in PHU funding per capita in 2013/2014, individuals from the lowest household income and between the ages of 50 and 64 years were 29% (95% CI: 1.10, 1.50) and 13% (95% CI: 1.03, 1.23) more likely to be vaccinated, respectively, while adjusting for confounders. No heterogeneous associations were observed in 2018/2019. CONCLUSION: Funding may have the potential to support PHU's role in preventing diseases, promoting health and reducing health inequities among the population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".