Physical Plant Design and Engineering Controls to Reduce Hospital‐Acquired Infections
Bibliographic record
Abstract
The importance of the environment as a reservoir for microorganisms implicated in disease transmission in the hospital setting has been increasingly recognized, especially with respect to dialysis units, ventilation in specialized areas, and the proper use of disinfectants (1). Inherent within the environmental setting is the importance of physical plant design. Several studies have underscored the importance of optimizing design standards to maximize patient and health care worker (HCW) safety, including the prevention of hospital‐acquired infections in patients (2‐6). Ulrich et al (7) recently completed an evidence‐based review, entitled ′The role of the physical environment in the hospital of the 21st century: A once-in-a-lifetime opportunity′ , for the Center for Health Design in California (USA), which was funded by the Robert Wood Johnson Foundation. Ulrich and colleagues identified over 600 studies that examined the hospital environment and its effects on staff effectiveness, patient safety, patient and family stress, quality and costs. They suggested that one of the important elements in improving patient safety is the reduction of the risk of hospital‐acquired infections through improved facility design.
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 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 | 0.003 |
| 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 | 0.001 |
| 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".