Evidence‐based assessment as an integrative model for applying psychological science to guide the voyage of treatment.
Bibliographic record
Abstract
Evidence-based assessment (EBA) streamlines literature reviewing and organizing clinical assessment by targeting the vital few topics, “satisficing,” and focusing on three major phases of clinical activity: prediction of diagnoses or other criteria, prescription of treatment or moderating factors, and process measurement. EBA is an organizing framework for applying a dozen steps to guide treatment. Technology is changing clinical assessment by increasing the efficiency and accuracy of scoring and feedback, as well as innovations that make more intensive assessment feasible. Fully implementing EBA suggests changes in training and requires a practice overhaul in exchange for greater efficiency, more accurate decisions, incrementally better outcomes, and increased service accessibility that could enable psychological science to help more people.
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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 | 0.190 | 0.210 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".