Consent and Participation: Ethical Issues in the Treatment of Children in Out-of-Home Care.
Bibliographic record
Abstract
Mental health service (MHS) providers confront questions of informed consent for evaluation and treatment of children in state custody who are placed in residential or foster care programs, where legal responsibility is shared between state and parent. There are ethical issues encountered by MHS providers who work with this growing population of children in placement. Matters of informed consent and access to information about treatment influence relationships with the parents, legal guardians, Child Protective Service workers, and the child. These specific concerns are addressed: informed consent, the right to be informed, and the rights of parents or foster carers to participate in a child's treatment. Recommendations for resolving dilemmas faced by MHS providers are discussed.
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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 | 0.215 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.002 | 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".