Knowledge and procedures of medical personnel about infection prevention in patients with chemotherapy-induced neutropenia
Bibliographic record
Abstract
Background: Chemotherapy-induced neutropenia significantly increases the risk of life-threatening infections in cancer patients, necessitating stringent infection prevention measures by medical personnel. Despite established protocols, gaps in knowledge and procedural adherence among healthcare workers persist, impacting patient outcomes. This study aimed to assess the knowledge and practices of medical personnel regarding infection prevention in neutropenic patients. Methods: A descriptive cross-sectional study was conducted involving 120 nurses from ICU, internal medicine, and oncology departments in a tertiary care hospital. Data were collected using a validated, self-administered questionnaire assessing demographic details, knowledge levels, and procedural adherence. Descriptive and inferential statistics were employed for analysis. Results: The majority of participants (45%) demonstrated good knowledge of infection prevention, while 11.7% scored poorly. Contaminated hands (93.3%) and inadequate hand hygiene (91.7%) were identified as primary infection sources. Procedural adherence was high for hand hygiene (80%) and PPE use (70.8%), but lower for patient education (60%) and isolation precautions (65%). ICU nurses exhibited the highest knowledge levels (50%), whereas oncology nurses had the highest proportion of poor knowledge (15%).Conclusion: While medical personnel generally possess adequate knowledge of infection prevention, inconsistencies in practice—particularly in patient education and isolation—highlight the need for targeted training and institutional reinforcement. Strengthening these areas is critical to improving patient safety and reducing infection-related morbidity in neutropenic individuals.
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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 | 0.010 |
| 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) | 0.002 | 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".