Knowledge and procedures of medical personnel about infection prevention in patients with chemotherapy-induced neutropenia
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: 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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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it