Hospital infections rate and health staff density in a Neonatology Department
Bibliographic record
Abstract
Objective. The aim of our study was to compare staffing trends for physicians and nurses with hospital infections (HIs) rate at a neonatology department of tertiary care health facility. Methods. The prospective cohort study was conducted at Neonatology Department, Clinical Centre Kragujevac, Serbia. The incidences of neonates with HIs were calculated for each of the quarters during the 5-year period. The data about the number and structure of staff for each quarter of the study period were collected from the official work schedule lists of the department. Results. This study registered a total of 272 HIs occurring in 264 neonates. The incidence rate of patients with HIs was 13.36% and the incidence density of HIs was 5.85 per 1,000 patient-days. In the observed period the trend of incidence rate of patients with HIs was stable, but the trend of incidence density of HIs was increasing, however without statistical significance. The highest incidence rates of patients with HIs were recorded in the third quarter of all observed years respectively: 17.44% in 2012, 16.74% in 2013, 23.47% in 2014, 18.18% in 2015 and 22.74 in 2016. The average number of nurses in the first shift ranged from 5.0 to 7.0, but in second shift was 3.0 to 4.0 only, and the least number of nurses worked in the third quarter of each year. The average number of physicians in the first shift ranged from 2.33 to 4.47 and they were also the fewest in numbers in the third quarter. We observed strong negative correlation between the average incidence rate of patients with HIs and the average number of nurses in the first shift (r=-0.977, p=0.023). Conclusion. Optimal nurse and physician staffing of neonatal departments in developing countries and the avoidance of inter-quarter variations in number of staff at duty would decrease the incidence of HIs and probably create overall savings of the hospital budget.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".