A systematic review of the emotional, behavioural and cognitive features exhibited by school‐aged children experiencing neglect or emotional abuse
Bibliographic record
Abstract
BACKGROUND: Interventions to minimize the long-term consequences of neglect or emotional abuse rely on prompt identification of these children. This systematic review of world literature (1947-2012) identifies features that children aged 5-14 years experiencing neglect or emotional abuse, as opposed to physical or sexual abuse, may exhibit. METHODS: Searching 18 databases, utilizing over 100 keywords, supplemented by hand searching, 13,210 articles were identified and 111 underwent full critical appraisal by two independent trained reviewers. RESULTS: The 30 included studies highlighted behavioural features (15 studies), externalizing features being the most prominent (8/9 studies) and internalizing features noted in 4/6 studies. Four studies identified attention deficit hyperactivity disorder (ADHD) associated features: impulsivity, inattention or hyperactivity. Child difficulties in initiating or developing friendships were noted in seven studies. Of 13 studies addressing emotional well-being, three highlighted low self-esteem, with a perception of external control (1), or depression (6) including suicidality (1). A negative internal working model of the mother increased the likelihood of depression (1). In assessing cognition or academic performance, lower general intelligence (3/4) and reduced literacy and numeracy (2) were reported, but no observable effect on memory (3). CONCLUSIONS: School-aged children presenting with poor academic performance, ADHD symptomatology or abnormal behaviours warrant assessment of neglect or emotional abuse as a potential underlying cause.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".