Influenza Immunizations in the Elderly: A Continuous Quality Improvement Project
Bibliographic record
Abstract
UNLABELLED: As part of the continuous quality improvement program at The Toronto Hospital's Department of Family & Community Medicine (TTH-DFCM), it was considered necessary to examine the structures, processes and outcomes of influenza immunization for the elderly. OBJECTIVE: The study sought to (a) document the current influenza immunization process; (b) quantify influenza immunization rates for elderly patients during two consecutive immunization seasons (1996 and 1997), and compare these rates across physician teams, attending staff vs. residents, patient gender, and patient age groups; (c) compare influenza immunization rates with other centers; and (d) identify barriers and propose solutions to improve influenza immunization rates in the elderly. DESIGN: Evaluation Formative Research. SETTING: A computerized roster of 15,000 patients at The Toronto Hospital, Department of Family and Community Medicine, a University of Toronto academic teaching center. PARTICIPANTS: Active patients age 65 years and over. DEPENDENT VARIABLE: Influenza immunization. INDEPENDENT VARIABLES: Physician Teams, Physician status, Patient gender, and Patient age group. RESULTS: Immunization rates of attendees increased from 75.4% to 78.7%; over 3% increase from 1996 to 1997. Major subgroups which benefited from increased immunization rates were patients in the Blue team, patients age 70-74 years, and female patients. CONCLUSION: This study presents a rigorous examination of the components of the influenza immunization program, and demonstrates improved immunization rates over a two-year period. Suggestions for future action have been identified. The study design can also serve as a model for future clinical quality improvement projects.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".