Best practice guidelines for the use and implementation of therapeutic interventions for children and young people in out of home care
Bibliographic record
Abstract
The development of these guidelines marks another significant step forward in the process of ensuring that services delivered to vulnerable children meet the highest possible standard.The process of developing these guidelines emanated from a request to this Board by the Office of the Minister for Children and Youth Affairs following representations from the Social Service Inspectorate.The concept of therapeutic intervention for children in out of home care settings is highly complex and has to date been subject to a myriad of interpretations and therefore great inconsistency in models of service delivery.This document attempts to unravel this complexity and make it easier for those responsible for planning, delivering and quality assuring such services to work from a common foundation.Our aim is that these guidelines will promote reflective practice and will add greatly to the body of knowledge around the complex area of therapeutic interventions.Developing these guidelines has not been easy and has involved many hours of research and intense deliberation by a group of very experienced practitioners, policy makers, inspectors and managers.I want to thank all those who participated as committee members, contributors, researchers and peer reviewers for their time, expertise and commitment.Particular thanks must go to Gráinne McGill, Advisory Officer, CAAB who skilfully and passionately steered this process resulting, I believe, in a guidance document that will underpin best practice in therapeutic interventions long into the future.
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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 | 0.165 | 0.307 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".