Bibliographic record
Abstract
n advancing a metasystems approach to evidence-based practice in mental health care for children, Kazak and her colleagues recently characterized the child mental health system in the United States as fragmented. They described it as being composed of at least six different sectors, broadly found to be administratively and fiscally segregated, of which child welfare and juvenile justice are but two (Kazak et al., 2010). Similar fragmentation and a host of associated problems have been described in the Canadian context (Manion, 2010). Among the many concerns arising from this state are the resulting challenges of providing needs-driven, evidence-based continuity of services for youth who populate multiple sectors and multiple levels within sectors, from secure residential placements down. Compounding this is the lack of consensus--both within and across disciplines--as to what criteria should be used to identify beneficial interventions and what interventions meet the standards (Weisz, Sandler, Durlak, & Anton, 2005, p. 642). Notwithstanding the identification of children and adolescents in both juvenile justice and child welfare systems as populations in pressing need of stronger foundations of evidence-based practices, Kazak et al. (2010) point out that evidence-based assessment methods and interventions do exist within these systems. But they suggest that for many personnel working within these systems, limited awareness of the evidence bases as well as limited opportunities for training mean many children and adolescents in need of mental health services go without (see also Wasserman et al., 2003). This is despite the well-documented high levels of mental health needs among children and adolescents in these systems. For example, estimates of adolescents with mental health problems in the child welfare system are as high as 50% (Burns et al., 2004; Garland et al., 2001), and up to 70% of adolescents in the U.S. juvenile justice system (across a range … Language: en
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".