Body Image Disturbance and Psychopathology in Children: Research Evidence and Implications for Prevention and Treatment
Bibliographic record
Abstract
Body image disturbance has been listed as a diagnostic feature of several psychopathological conditions [1-5]. As body image concerns appear in children as young as 5 years old, it was hypothesized to be an important risk factor for the development of psychopathology, such as Eating Disorders (ED) in adolescence. Results of the current review of the literature support this hypothesis and suggest that young children can (1) estimate as accurately as adults their body size, and (2) show similar cognitive distortions, cognitive biases, and the negative emotions associated with their body image. There is some evidence that children can (1) display severe symptoms of body image distortion such as is present in body dysmorphic disorder (BDD), (2) demonstrate negative stereotypes toward obesity and (3) internalize thin ideal body image. Research suggests that some of the key factors involved in childrens body satisfaction is parental perception and direct comments from peers. Body dissatisfaction can predict body image related psychopathology later in development. In conclusion, intervention and prevention of eating disorders with children under the age of 7 is arguably a viable strategy, and cognitive and behavioural training could involve parents, teachers, mental health practitioners as well as the child him/herself. This training and education should cover early manifestation of body image disturbance and their associated risk factors. Keywords: Literature review, body image, children, research, treatment
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".