Improving rates of pneumococcal vaccination on discharge from a tertiary center medical teaching unit: A prospective intervention
Bibliographic record
Abstract
BACKGROUND: Pneumococcal disease causes significant morbidity and mortality in at-risk individuals, and is complicated by emerging antibiotic resistance. An effective, safe and cost-effective vaccine is available, but despite this many patients who would benefit from pneumococcal vaccination remain unvaccinated. The purpose of this study was to determine the rates of missed opportunities to provide pneumococcal vaccination to patients being discharged from a tertiary center medical teaching unit and to determine if a nurse coordinator-based intervention would increase rates of pneumococcal vaccination prior to discharge home. METHODS: We conducted a prospective, controlled study in the setting of a Medical Teaching Unit at a tertiary care centre to assess the impact of a nurse coordinator based intervention on the rates of vaccination of eligible patients on discharge home. The rates of vaccination during an eight-week usual-care period (February 20 to April 16, 2002) and an eight-week intervention period (April 22 to June 16, 2002) were compared. RESULTS: Prior to the intervention none of thirty-eight eligible patients were vaccinated prior to discharge home from the Medical Teaching Unit. After the intervention 27 (54%) of fifty eligible patients were vaccinated prior to discharge. CONCLUSION: There are significant missed opportunities to provide pneumococcal vaccination to inpatients who are discharged home from a medical unit. Using a patient care coordinator we were able to significantly improve the rates of vaccination on discharge.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".