Introducing a randomized controlled trial into Family Proceedings: Describing the ‘how?’ and defending the ‘why?’
Bibliographic record
Abstract
Abstract In 2011, a randomized controlled trial (RCT) of a mental health intervention for families with children under the age of 5 years coming into the Scottish care system was launched, called the Best Services Trial (BeST). When attempts were made to expand the study to English sites, the local leadership Judge objected, concerned that randomization in family proceedings was unfair, potentially discriminatory, and unlawful. Considerations about parental consent, fairness of randomization, and an understanding that the new intervention might be no better, or even harmful, compared to current best practices were crucial in addressing these concerns. In 2017, BeST was launched in England utilizing a randomized methodology. Significant input into the design of BeST came from the leadership Judge who had previously considered randomization unlawful. In July 2021, 383 families with 488 children had been recruited across both Scottish and English sites. Follow-up continues and 76 per cent of families continue to participate at 2.5 years after entering the study. Although there were undoubted challenges in designing and implementing BeST, with hindsight, the objections raised to the testing of interventions randomly were demonstrably resolvable and the process of randomization encountered no legal challenges. This is the first time an RCT has been accommodated within live proceedings in the family justice arena in England and Wales and one of a relatively few such RCTs conducted internationally.
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.
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 | 0.626 | 0.654 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.029 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".