Bibliographic record
Abstract
Over the past quarter century, researchers have developed a body of parent training programs that have proven effective in reducing child behavior problems, but few of these have made their way into routine practice. This article describes the long and winding road of implementation as applied to children's mental health. Adopting Rogers' (1995) diffusion framework and Fixsen and colleagues' implementation framework (Fixsen, Naoom, Blase, Friedman, & Wallace, 2005), we review more than a decade of research on the implementation of Parent Management Training-Oregon Model (PMTO). Data from U.S. and international PMTO implementations are used to illustrate the payoffs and the challenges of making empirically supported interventions routine practice in the community. Technological advances that break down barriers to communication across distances, the availability of efficacious programs suitable for implementation, and the urgent need for high quality mental health care provide strong rationales for prioritizing implementation. Over the next quarter of a century, the challenge is to reduce the prevalence of children's psychopathology by creating science-based delivery systems to reach families in need, everywhere.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.192 | 0.095 |
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".