Knowledge of Shaken Baby Syndrome among Hospital Nurses in Erbil City
Bibliographic record
Abstract
Background and objectives: Shaken baby syndrome and pediatric abuse head trauma are the most common causes of mortality and morbidity due to physical child abuse. Nurses have a main role in parents’ education regarding child abuse prevention. This study aimed to assess nurses’ knowledge regarding shaken baby syndrome in Erbil City. Methods: A descriptive study was conducted at postpartum units, the delivery room and the ward at the Maternity Teaching Hospital, and the inpatient and intensive care units at Rapareen Pediatric Teaching Hospital in 2017 in Erbil City. A purposive sample of 50 nurses was recruited to the study. The data collection was performed using a questionnaire for interviewing the study participants, and the data were analyzed using descriptive and in-ferential statistical analysis. Results: The study findings revealed that the majority of the study participants were 19-25 years old and most did not have enough knowledge regarding the signs and symptoms of the shaken baby syndrome (irritability, lethargy, poor feeding breathing problems, uncon-trollable crying, vomiting, bluish skin, changes in sleeping pattern, convulsions or seizures and unresponsiveness). Nurses also had insufficient knowledge about the risk factors of this condition. Only a quarter of nurses knew that domestic violence is a risk factor and less than a quarter of them recognized depression and substance abuse of the caregiver as a risk factor. Regarding knowledge of the complications, the study found that a quarter of nurses knew that brain damage, cerebral palsy and blindness are complications of the shaken baby syndrome. Conclusions: Majority of nurses had poor knowledge about causes, signs, symptoms, risk factors and complications of the shaken baby syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".