Training and Experience: Keys to Enhancing the Utility for Foster Parents of the Assessment and Action Record from Looking after Children
Bibliographic record
Abstract
The Looking After Children (LAC) approach is now widely used internationally in child welfare. The approach, which originated almost two decades ago, aims systematically to raise the standard of corporate parenting and improve the outcome of young people in out-of-home care. The Assessment and Action Record (AAR) from LAC is used to monitor young people's developmental progress on a year-to-year basis. Clearly, foster carers are central to the successful implementation of LAC and it is important that they perceive the AAR to be useful in carrying out their fostering duties. Previous research in the UK and Australia found that foster carers believed the record to be useful, especially if they were just getting to know the child or if the child had been in multiple placements. The study reported by Sarah Pantin and Robert Flynn draws on survey information provided by 93 foster carers in the province of Ontario, Canada. They found that foster carers who had received what they saw as higher-quality training rated the AAR as being more useful in their work. Interestingly, however, the amount of experience they had had in using the instrument was unrelated to their ratings of its usefulness. Overall, high-quality training emerged as a central feature of effective implementation. Specific recommendations were made in relation to LAC training curriculum requirements and stakeholder involvement.
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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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".