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Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: It has been more than 15 years since infection control was first introduced in Korea, but there is little information available on the status of infection control program in the country. \n\nMETHODS: Included in the study were 139 acute care hospitals with more than 300 inpatient beds. A questionnaire, modified from US SENIC (Study on the Efficacy of Nosocomial Infection Control) and Canadian RICH (Resources for Infection Control in Canadian Acute Care Hospitals) survey, was mailed to the hospitals in the winter of 2003. \n\nRESULTS: Ninety-eight (70.5%) of 139 hospitals responded. There was an average of 1.2 (SD, 0.7) Infection Control Practitioners (lCPs) in each hospital and 95.7% were nurses and only 56.5% of the ICPs worked as full-time. The 71.4% of the hospitals had a position for Infection Control Doctor. All hospitals had an Infection Control Committee, which met an average of 3.7 (SD, 1.7) times a year. The 85.7% of the hospitals performed surveillance, but only 31.6% were monitoring surgical site infections. Review of microbiology data was the most common method for case-finding. More than 90% of the hospitals had infection control policies and guidelines, but an adherence to the policies and guidelines was not monitored regularly. \n\nCONCLUSION: This study reports the first comparable profile of infection control program of general acute care hospitals in Korea. Although the foundation for infection control program appears to have been established, there is the need for a further increase in the number of ICPs, the standardization of the surveillance method, and the promotion of adherence to the infection control guidelines.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it