Physical Child Abuse: What Are the External Factors?
Bibliographic record
Abstract
Children are an integral asset for future development of a nation. In lieu with this notion, countries pay much attention to their development and well being. In recent times, child abuse has been rampant. This issue is constantly making headlines in national newspapers in Malaysia. The alarming effect of this phenomenon is its inevitable implication on the family institution and the nation’s aspiration to create a better tomorrow. This current study is an attempt to discover the contributory factors of physical child abuse in Malaysia. The objective of this study was to identify the background and demography of abuser and to identify the external factors that cause a parent to abuse their child. This case study was conducted in Selangor amongst eight Malay respondents using an in-depth interview session. This study found that external factors as the major contributory factor to physical child abuse. The factors are financial constraints, family crisis, character and disobedience of the children or so called ‘deviant children’, surroundings of the home, conflict with neighbours or lack of social support, parent’s mental illness and and influence of alcohol. Hence these factors are articulated and interpreted by the researcher. Implications of this research, profession who work with children such as social worker, psycologist and counselor should look into the external factors that contribute to the physical child abuse in designing intervention, cure and effective strategies to overcome this problem. In future, protecting children needs collective effort from policy maker, authority body, non-government organization and member of society.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".