Use of, Effectiveness of, and Attitudes Regarding Influenza Vaccine Among House Staff
Bibliographic record
Abstract
OBJECTIVE: To determine influenza vaccination rates, vaccine effectiveness, and factors influencing vaccination decisions among house staff. DESIGN: Cross-sectional survey. SETTING AND PARTICIPANTS: All residents registered at the University of Toronto were surveyed after the 1999-2000 influenza season. Of the 1,159 questionnaires mailed, 670 (58%) could be evaluated. RESULTS: Influenza-like illnesses were reported by 36% of house staff. The vaccination rate was 51% among respondents, being highest for community and occupational medicine and pediatric staff (77% and 75%) and lowest for psychiatry, surgery, and radiology staff (32%, 36%, and 36%). Vaccinees reported significantly fewer episodes of illness (42 vs 54 per 100 subjects; P = .03) and fewer days of illness (272 vs 374 per 100 subjects; P = .02); absenteeism was not different (63 vs 69 per 100 subjects; P = .69). Self-protection was the most common reason for vaccination. Vaccinees believed the vaccine was more effective than did non-vaccinees (P < .01). Non-vaccinees considered influenza-like symptoms the most important side effect of the vaccine. Busy schedules and inconvenience were the most common reasons for not getting vaccinated. Overall, 44% of house staff believed the influenza vaccine should be mandatory. CONCLUSIONS: Influenza-like illness was common among house staff. They tended to work through their illnesses, potentially putting patients at risk. They were motivated mostly by self-protection and did report a benefit. Despite busy schedules and an unfounded fear of getting influenza symptoms from the vaccine, many thought the vaccine should be mandatory.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".