Meaningful Intervention with Children and Youth: A Reflection on Ten Years of Inquiry
Bibliographic record
Abstract
It has been known since the early 1970s that youth risk assessment does not necessarily assist us in determining youth needs and services. Still, where young people and crime are the concerned, interventions are often focused on risk assessment rather than need assessment, especially when these young people face incarceration. In this article we emphasize needs assessment and the development of a youth friendly approach to such assessment. We draw on a number of community-based and community involved studies that were conducted over a ten-year period, studies that focused on the perspectives, experiences, and needs of children and youth, and present as key among these studies a project on the development of a gender-sensitive tool for needs assessment that can aid workers with youth engagement and needs focused intervention.
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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 | 0.114 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.037 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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".