Assessment of knowledge, attitude, and practice of child abuse amongst health care professionals working in tertiary care hospitals of Karachi, Pakistan
Bibliographic record
Abstract
INTRODUCTION: UNICEF report (2004) states that a significant percentage of total child population under the age of 5 years suffered malnutrition. Child sexual abuse remains undiscussed across Pakistan. Health care professionals (HCPs) are usually the first notifiers of child abuse and are ethically obliged to manage and report it. OBJECTIVE: This study was conducted to assess HCPs' response in dealing with patients of child abuse. With a better understanding, we can have a better outcome for the victims. METHODS: A total of 101 participants filled out a structured questionnaire by HCPs working in three tertiary hospitals of Karachi i.e., Aga Khan University, National Institute of Child Health (NICH), and Civil Hospital. Data were entered into SPSS 19.0. RESULTS: HCPs believed that young male relatives were thought to be most likely the offender, and that every child regardless of class is prone to get abused triggered by financial stressors and the absence of parents. Proper physical exams helped identify cases. A proper system of reporting was required in hospitals, but HCPs were reluctant to report the cases to authorities. There was a significant difference noted between public and private hospitals. CONCLUSION: Our findings indicate that HCPs have limited knowledge in defining various types of abuse and most were unaware of any reporting facility in hospitals. Senior HCPs as consultants have a better understanding of child abuse than nurses or interns. Mandatory reporting should be implicated so that prompt action could be taken. There could be a more successful outcome of managing a child abuse victim with proper training.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".