Evidence-Based Assessment of Child and Adolescent Disorders: Issues and Challenges
Bibliographic record
Abstract
The main purpose of this article and this special section is to encourage greater attention to evidence-based assessment (EBA) in the development of a scientifically supported clinical child and adolescent psychology. This increased attention is especially important in light of (a) the omission of assessment considerations in recent efforts to promote evidence-based treatments for children and (b) ongoing changes in the nature of clinical child assessment. We discuss several key considerations in the development of guidelines for EBA, including the purposes of assessment, the role of disorder or problem specificity, the scope of assessment, assessment process parameters, possible "cross-cutting" assessment issues, psychometric considerations, and issues related to the clinician's integration of assessment data. We conclude the article with suggestions for how current, summary information on EBA can be developed, maintained, and disseminated.
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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 | 0.409 | 0.633 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".