Prevalence and determinants of tuberculin reactivity among physicians in Edmonton, Canada: 1996–1997
Bibliographic record
Abstract
BACKGROUND: Health care workers (HCW) have historically borne a heavy burden of tuberculosis (TB) infection and disease. Unfortunately, physicians are rarely included in HCW surveys of tuberculin exposure and infection. METHODS: The prevalence and risk factors for tuberculin reactivity were determined for a sample of the 1732 licensed physicians in Edmonton. Stratified random sampling was used to select 554 specialists and 219 general practitioners. These physicians were contacted by means of an introductory letter and a follow-up telephone call to solicit participation. All eligible physicians were asked to complete a questionnaire and those with either no recorded positive tuberculin test or a previously negative result were two-step tuberculin skin tested. RESULTS: In total, 560 physicians (72.4 %) participated in the study. The overall tuberculin reactivity for this population was 45.9%. Using logistic regression analysis, we determined that risk factors for reactivity were aged over 45 years, of foreign-birth, previous Bacillus Calmette-Guérin (BCG) vaccination, foreign practice experience, and being a respiratory medicine specialist. CONCLUSION: The prevalence of tuberculin reactivity among physicians is considerably higher than estimates for the general Canadian population. This observed excess risk may be associated with factors linked to their medical practice. The high participation rate suggests physician willingness to participate in this type of research, and emphasizes the need to include them in routine HCW surveillance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".