The Impact of Health Care Restructuring on Nosocomial Infections and Transmission of Antimicrobial Resistant Organisms
Bibliographic record
Abstract
The past decade has seen a dramatic change in Canadian health care as the treatment of many patients shifts from the hospital setting to the home or other alternative health care settings (1). Complex advances in technology and significant changes in the funding environment have also precipitated many changes in the functional capacity of our health care system. The portion of the total Canadian health care expenditures that has been allocated to hospitals and other institutions has declined steadily over the past two decades (2,3). This reduction in institutional‐based funding reflects the need to develop a system that improves the use of out‐of‐hospital sites for delivery of health care services and decreases reliance on the specific institutional component of the health care sector. Recent data reveal that Canadian hospital discharge rates have decreased by 14% between 1994 and 1998, and there has been a reduction in the length of stay from 7.4 to 7 days (4). In conjunction with a reduction in the number of hospital beds, there has been a massive shift in the proportion of surgeries that are performed on an outpatient basis. Further developments in the delivery of care include the increased use of telehealth, which allows health care professionals to provide increasingly complex services centralized hospital sites to outlying areas.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".