A Systematic Review of Mental Health Disorders of Children in Foster Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: This article summarizes the rate of mental health disorders of foster children, the specific types of disorders faced by this population, and how factors such as type of abuse or placement variables can affect mental health outcomes. METHOD: A search in PsycInfo Ovid, EMBASE Elsevier, and Cochrane Library Wiley resulted in 5,042 manuscripts that were independently reviewed by two authors, yielding 25 articles. INCLUSION CRITERIA: Published in or after 2000, written in English, and having a population sample of foster children (ages 0-18) in Western countries including the United States, Norway, Australia, and Canada. RESULTS: Foster children have higher rates of mental health disorders than those of the general population. The most common diagnoses include oppositional defiant disorder/conduct disorder, major depressive disorder, post-traumatic stress disorder, and reactive attachment disorder. Variables such as type of maltreatment and type of placement predicted mental health outcomes. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Children in foster care experience more mental health disorders, as a response to either the circumstances that led to being removed from their homes or the experience of being placed in foster care. These results demonstrate the necessity for providers to consider mental health issues when caring for children in foster care and to perform appropriate screenings and assessments. With adequate trauma-informed training, providers can quickly become comfortable and competent in identifying mental health needs of children in foster care who have experienced trauma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it