Shea/Apic Guideline: Infection Prevention and Control In The Long-Term Care Facility
Bibliographic record
Abstract
Long-term care facilities (LTCFs) may be defined as institutions that provide health care to people who are unable to manage independently in the community. 1This care may be chronic care management or short-term rehabilitative services.The term nursing home is defined as a facility licensed with an organized professional staff and inpatient beds that provides continuous nursing and other services to patients who are not in the acute phase of an illness.There is considerable overlap between the 2 terms.More than 1.5 million residents reside in United States (US) nursing homes.In recent years, the acuity of illness of nursing home residents has increased.LTCF residents have a risk of developing health care-associated infection (HAI) that approaches that seen in acute care hospital patients.A great deal of information has been published concerning infections in the LTCF, and infection control programs are nearly universal in that setting.This position paper reviews the literature on infections and infection control programs in the LTCF.Recommendations are developed for long-term care (LTC) infection control programs based on interpretation of currently available evidence.The recommendations cover the structure and function of the infection control program, including surveillance, isolation precautions, outbreak control, resident care, and employee health.Infection control resources are also presented.Hospital infection control programs are well established in the US.Virtually every hospital has an infection control pro-
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".