The lesser evil for youth with behavioural problem: Basis for decision-making on time-out placements in child welfare
Bibliographic record
Abstract
When faced with the behavioural problems of certain youth placed in a youth residential care centre, the use of more restrictive measures remains delicate and based on complex decision-making. While many studies have looked at the decision-making process in child welfare, very few have looked at time-out placements. Time-out placements are very short-term placements with the main objective of providing a temporary break between the context in which the youth is acting out and their environment, while avoiding a definitive move to another resource. Thus, the present study documents the decision-making process that accompanies this measure. Four group interviews with 5 child welfare practitioners (n = 20) involved in the decision-making process preceding a time-out placement were conducted with clinical scenarios. The study employs the Decision-Making Ecology framework both as a conceptual framework and as a basis for data analysis. The analytical approach is inspired by the qualitative consensus approach. The results reveal certain observations regarding the tolerance threshold of the child welfare practitioners before turning to a time-out type of intervention. While the accumulation of risk factors among youth can serve as a justification, certain inconsistencies between sometimes vague intervention objectives and various organizational issues are more of an obstacle. In such a context, the decision to resort to a time-out placement becomes a lesser evil as it is perceived as the “least worst” possible option.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".