Healthcare-Associated Infections as Patient Safety Indicators
Bibliographic record
Abstract
Healthcare-associated infections (HAIs) are a pressing and imminent patient safety concern as they cause substantial preventable morbidity and mortality. Despite this, there is a strong tendency for healthcare administrators and providers to view them as far less of a threat to patient safety than adverse events such as medication administration errors and falls. Further, validated strategies to prevent HAIs are frequently slow to be adopted. This paper reviews two HAIs of increasing visibility and importance - namely, methicillin-resistant Staphylococcus aureus and Clostridium difficile - and discusses the pivotal importance of hand hygiene and environmental cleaning in their prevention. Possible reasons why HAIs are approached differently from other patient safety issues are discussed, including the false sense of security created by the advent of antibiotics, the lack of randomized controlled trials supporting infection-control interventions and the systemic multifactorial causes of HAIs that result in a need for interventions that go far beyond traditional clinical boundaries. Suggested strategies to improve patient safety with respect to HAIs are provided, including a focus on the role of potential links to accreditation; the role of public reporting; healthcare facility design; change management strategies; visible leadership and role modelling; collaboration between facilities and with public health; reducing hospital overcrowding; and accountability and funding. Finally, the impact of the burgeoning interest of the media, the threat of legal liability and the well-being of healthcare providers are discussed.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".