A national multi-centre study of accuracy of hand hygiene of health care workers in ICUs
Bibliographic record
Abstract
OBJECTIVE To investigate the accuracy of hand hygiene of the health care workers in ICUs so as to provide scientific basis for further improvement of accuracy of hand hygiene.METHODS A multi-centre study was conducted from Oct 2013 to Sep 2014,totally 66 ICUs from 47 hospitals were included in the study,the uniform questionnaire was adopted,and the accuracy of hand hygiene of the health care workers was investigated every month according to the Medical Personnel Hand Hygiene Norms.All of the data were input into the EXCEL table,and the statistical analysis was performed with the use of SPSS 17.0software.RESULTS The rate of correct hand hygiene of the health care workers from 47 hospitals was 83.48%,and the rate of correct hand hygiene of the health care workers from the hospitals in eastern China was highest(89.08%);the rate of correct hand hygiene was highest(89.05%)among the health care workers from the ICUs of internal medicine departments and was lowest(67.34%)among the health care workers from the ICUs of other departments.The rate of correct hand hygiene of the health care workers was lowest(52.83%)in the fourth quarter of 2013,and the rate of correct hand hygiene was increased and stable in 2014.CONCLUSIONThe rate of correct hand hygiene of the health care workers varies in the region,ICU,and quarter.There is still some room for improvement of hand hygiene accuracy of the health care workers.It is necessary to strengthen the hand hygiene management in the key departments and key links.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".