Prevalence and predictors of influenza vaccination in long-term care homes: a cross-national retrospective observational study
Bibliographic record
Abstract
OBJECTIVE: To compare facility-level influenza vaccination rates in long-term care (LTC) homes from four countries and to identify factors associated with influenza vaccination among residents. DESIGN AND SETTING: Retrospective cross-sectional study of individuals residing in LTC homes in New Brunswick (Canada), New Zealand, Switzerland, and the Netherlands between 2017 and 2020. PARTICIPANTS: LTC home residents assessed with interRAI assessment system instruments as part of routine practice in New Brunswick (n=7006) and New Zealand (n=34 518), and national pilot studies in Switzerland (n=2760) and the Netherlands (n=1508). End-of-life residents were excluded from all country cohorts. OUTCOMES: Influenza vaccination within the past year. RESULTS: Influenza vaccination rates among LTC home residents were highest in New Brunswick (84.9%) and lowest in Switzerland (63.5%). For all jurisdictions where facility-level data were available, substantial interfacility variance was observed. There was approximately a fourfold difference in the coefficient of variation for facility-level vaccination rates with the highest in Switzerland at 37.8 and lowest in New Brunswick at 9.7. Resident-level factors associated with vaccine receipt included older age, severe cognitive impairment, medical instability, health conditions affecting a greater number of organ systems and social engagement. Residents who displayed aggressive behaviours and smoke tobacco were less likely to be vaccinated. CONCLUSION: There are opportunities to increase influenza vaccine uptake at both overall country and individual facility levels. Enhanced vaccine administration monitoring programmes in LTC homes that leverage interRAI assessment systems should be widely adopted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".