The problem of professional hemocontact infections in the Saratov Region
Bibliographic record
Abstract
Introduction. The increase in the number of emergency situations (ES) in medical institutions in Russia actualizes the study of the state of this problem in different territories in order to optimize regional programs for the prevention of occupational diseases of medical workers, including hemocontact infections. The aim of study is to identify possible reasons for the lack of occupational morbidity of hemocontact infections in the Saratov Region. Materials and methods. To determine the frequency, types and possible causes of ES, the procedure for their registration, and the awareness of medical workers about the algorithm of actions in case of their development, 82 doctors in the specialties of obstetrician-gynecologist and surgeon (39 and 43 people, respectively) were interviewed at a multi-specialty hospital in Saratov (continuous sampling method). Results. The presence of ES in the practice of the majority of doctors surveyed (59%) was revealed, and 19% of respondents had multiple as (5 or more times). Most often, as occurs during the transfer and reception of acute surgical instruments. Among the most likely risk factors for developing as are high workload, emotional overload, and working at night. A quarter of aces do not register in the injury log, which is due to lack of time (42%) and the log itself (25%), ignorance of the need for this action (17%), fear of dismissal (8%). Conclusions. One of the main possible reasons for the lack of professional incidence of hemocontact infections is the lack of awareness of doctors about the algorithm of action in the event of an emergency at the workplace, in some cases-the lack of conditions for the implementation of this algorithm.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".