ROLLING OUT SNAP® AN EVIDENCE-BASED INTERVENTION: A SUMMARY OF IMPLEMENTATION, EVALUATION, AND RESEARCH
Bibliographic record
Abstract
<p>This article describes the evolutionary process of developing, disseminating, and implementing an evidence-based intervention model for children in conflict with the law. Stop Now And Plan (SNAP<sup>®</sup>), a Canadian, evidence-based gender sensitive model for young children in conflict with the law, was initiated in 1985 in response to the de-criminalization of children under 12 in Canada. This community-based model is well validated for its efficacious outcomes on reducing problem behaviours in this high-risk population, helping to shift the trajectory of criminal outcome. The article describes the lessons learned during the evaluation, implementation, and replication of SNAP<sup>®</sup> and the resulting creation of a stringent implementation approach. Currently under the management of the Centre for Children Committing Offences (CCCO), replication sites known as SNAP<sup>® </sup>Affiliates, enter into a formalized licensing agreement that includes assessing site readiness and theoretical philosophy, ongoing training and consultation, and an accreditation quality assurance process. This formalized approach has been adopted to ensure sites are able to deliver the highest quality of service and to replicate successful outcomes, changing life course trajectories of these high-risk children and families.</p>
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 | 0.200 | 0.192 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".