The Foster Care Systems are Failing Foster Children: The Implications and Practical Solutions for Better Outcomes of Youth in 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
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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