<i>Outbreak Investigation, Prevention and Control in Health Care Settings: Critical Issues in Patient Safety, 2nd Edition</i>Outbreak Investigation, Prevention and Control in Health Care Settings: Critical Issues in Patient Safety, 2nd Edition By Kathleen Meehan Arias Jones and Bartlett Publishers, Sudbury, Massachusetts, 2010. 435 pp. $75.95 (paperback)
Bibliographic record
Abstract
Outbreak Investigation, Prevention and Control in Health Care Settings: Critical Issues in Patient Safety, 2nd edition , written by Kathleen Meehan Arias, is a concise overview of epidemiologic principles applied to health care facilities and a reference for outbreak identification, investigation, prevention, and control. The first chapter provides an introduction to epidemiology, including definitions, etiologic agents of disease, and a comparison of methods of epidemiology. It is well organized with up-to-date tables and graphs to supplement or illustrate information presented in the text. Each subsequent chapter also makes good use of supporting tables, figures, graphs, and formulas. This chapter and all subsequent chapters present references and resources, including textbooks, journals, and Web-based information as current as 2008. Chapter 2, “Surveillance Programs, Public Health, and Emergency Preparedness,” was coauthored with Lorraine Messinger Harkavy. It provides definitions of surveillance in hospital and nonhospital health care settings; methods for surveillance; and guidelines for developing and evaluating surveillance programs in acute care, ambulatory care, and long-term care settings. The discussion about determining the process for data collection, including how to calculate rates and analyze data, provides useful formulas for calculating incidence rate and prevalence rates. Also mentioned is risk stratification and the use of other resources, including external databases. The subsequent 3 chapters deal with outbreaks in acute care, long-term care, and ambulatory care settings. Topics include outbreaks attributable to devices, products, or procedures; specific organisms; modes of transmission (eg, airborne); environmental reservoirs (eg, legionellosis); and disease syndromes (eg, gastroenteritis). For organism-specific outbreaks, there is additional infection control information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.112 | 0.074 |
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".