The Foster Care Systems are Failing Foster Children: The Implications and Practical Solutions for Better Outcomes of Youth in Care
Bibliographic record
Abstract
Although the foster care systems in North America are set up with good intentions for best practices for foster children, in reality these systems are failing youth in care. Many foster children experience more psychological, social, educational, behavioural, and emotional problems as compared to children who are not in foster care, and this can continue into adulthood. Attachment theory can help to explain why some children experience these problems. Professionals who work with this population need to have a good understanding of foster children’s unique experiences in order to help them as much as possible. Literature has addressed the problems that foster children have faced for decades, but there seems to be little change that happens to address and prevent these problems. There is no doubt that there is a great need for change in the current foster care systems in North America because current outcomes for many foster children are negative. This paper reviews the literature on foster care and explains the issues that foster children experience. It also addresses why the foster care system is failing youth, and gives practical suggestions for solutions.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".