Evidence-Based Practice Manual: Research and Outcome Measures in Health and Human Services
Bibliographic record
Abstract
Abstract The Evidence-Based Practice Manual includes 104 original chapters, each specially written by the most prominent and experienced medical, public health, psychology, social work, criminal justice, and public policy practitioners, researchers, and professors in the United States and Canada. This book is specifically designed with practitioners in mind, providing at-a-glance overviews and direct application chapters. This is the only interdisciplinary volume available for locating and applying evidence-based assessment measures, treatment plans, and interventions. Particular attention has been given to providing practice guidelines and exemplars of evidence-based practice and practice-based research. The Evidence-Based Practice Manual emphasizes and summarizes key elements, issues, concepts, and how-to approaches in the development and application of evidence-based practice. Discussions include program evaluation, quality and operational improvement strategies, research grant applications, validating measurement tools, and utilizing statistical procedures. Concise summaries of the substantive evidence gained from methodologically rigorous quantitative and qualitative research provide make this is an accessible resource for a broad range of practitioners facing the mandate of evidence-based practice in the health and human services.
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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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.048 | 0.034 |
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".