Bibliographic record
Abstract
Evidence-based assessment (EBA) emphasizes the use of research and theory to inform the selection of assessment targets, the methods and measures used in the assessment, and the assessment process itself. Our review focuses on efforts to develop and promote EBA within clinical psychology. We begin by highlighting some weaknesses in current assessment practices and then present recent efforts to develop EBA guidelines for commonly encountered clinical conditions. Next, we address the need to attend to several critical factors in developing such guidelines, including defining psychometric adequacy, ensuring appropriate attention is paid to the influence of comorbidity and diversity, and disseminating accurate and up-to-date information on EBAs. Examples are provided of how data on incremental validity and clinical utility can inform EBA. Given the central role that assessment should play in evidence-based practice, there is a pressing need for clinically relevant research that can inform EBAs.
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.
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 | 0.122 | 0.392 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.039 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".