Evidence-Based Practice Manual: Research and Outcome Measures in Health and Human Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it