Correlation of long-term care facility vaccination practices between seasons and resident types
Bibliographic record
Abstract
BACKGROUND: Influenza vaccination varies widely across long-term care facilities (LTCFs) due to staff behaviors, LTCF practices, and patient factors. It is unclear how seasonal LTCF vaccination varies between cohabitating but distinct short-stay and long-stay residents. Thus, we assessed the correlation of LTCF vaccination between these populations and across seasons. METHODS: in 2013-2014 (3,042,881 residents; 15,683 LTCFs) and 2014-2015 (3,143,174, residents; 15,667 LTCFs). MDS-assessed influenza vaccination was the outcome. Pearson correlation coefficients were estimated to assess seasonal correlations between short-stay and long-stay resident vaccination within LTCFs. RESULTS: The median proportion of short-stay residents vaccinated across LTCFs was 70.4% (IQR, 50.0-82.7%) in 2013-2014 and 69.6% (IQR, 50.0-81.6%) in 2014-2015. The median proportion of long-stay residents vaccinated across LTCFs was 85.5% (IQR, 78.0-90.9%) in 2013-2014 and 84.6% (IQR, 76.6-90.3%) in 2014-2015. Within LTCFs, there was a moderate correlation between short-stay and long-stay vaccination in 2013-2014 (r = 0.50, 95%CI: 0.49-0.51) and 2014-2015 (r = 0.53, 95%CI: 0.51-0.54). Across seasons, there was a moderate correlation for LTCFs with short-stay residents (r = 0.54, 95%CI: 0.53-0.55) and a strong correlation for those with long-stay residents (r = 0.68, 95%CI: 0.67-0.69). CONCLUSIONS: In LTCFs with inconsistent influenza vaccination across seasons or between populations, targeted vaccination protocols for all residents, regardless of stay type, may improve successful vaccination in this vulnerable patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".