Using the Hierarchy of Control Technologies to Improve Healthcare Facility Infection Control: Lessons From Severe Acute Respiratory Syndrome
Bibliographic record
Abstract
Health care facilities need to review their infection control plans to prepare for the possible resurgence of severe acute respiratory syndrome, other emerging pathogens, familiar infectious agents such as tuberculosis and influenza, and bioterrorist threats. This article describes the classic "hierarchy of control technologies" that was successfully used by occupational and environmental medicine professionals to protect workers from illness and death during the resurgence of tuberculosis in the 1990s. Also discussed are related guidelines from building and equipment professional organizations and novel infection control techniques used successfully by various hospitals in Asia, Canada, and the United States during the 2003 severe acute respiratory syndrome epidemic. Taken together, they suggest a framework upon which a comprehensive infection control plan can be crafted to prevent the spread of deadly infectious agents to health care workers (clinicians and paraprofessionals), uninfected patients and visitors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".