Evaluating clinical significance through equivalence testing: Extending the normative comparisons approach
Bibliographic record
Abstract
The field of psychology, as with many other disciplines, has been increasingly interested in being able to measure the effectiveness of behavioral interventions. This trend has led to a number of different approaches for measuring clinical significance, each addressing a slightly different aspect of the clinical outcome. Recently, clinical psychologists (and clients) have supported the contention that one of the most important therapeutic questions is whether clients are functioning equivalently to normal controls following an intervention. To address this question, Kendall, Marrs-Garcia, Nath, and Sheldrick (1999) presented an approach to measuring clinical significance that utilizes tests of equivalence. The present study clarifies the nature of the hypotheses being conducted in measuring clinical significance with tests of equivalence and extends the approach by incorporating recent advances in equivalence testing. A revised approach for evaluating clinical significance via equivalence testing is proposed, and an empirical example demonstrating this approach is provided.
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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 | 0.256 | 0.629 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".