Adopting a Conceptual Validity Framework for Testing in Speech-Language Pathology
Bibliographic record
Abstract
Purpose Limited evidence-based guidelines for test selection continue to result in inconsistency in test use and interpretation in speech-language pathology. A major barrier is the lack of explicit and consistent adoption of a validity framework by our field. In this viewpoint, we argue that adopting the conceptual validity framework in the Standards for Educational and Psychological Testing (American Educational Research Association et al., 2014) would support both the development of more meaningful and feasible clinical tests and more appropriate use and interpretation of tests in speech-language pathology. Method We describe and evaluate the Standards for Educational and Psychological Testing (American Educational Research Association et al., 2014) validity framework and consider its relevance to speech-language pathology. We describe how the validity framework could be integrated into clinical practice and include examples of how it could be applied to support common clinical decisions. We evaluate the costs and benefits of adopting this framework, from the perspectives of speech-language pathologists, clients, and test developers. Results The Standards' validity framework clarifies complex validity issues by shifting the focus of validity from tests to the decisions speech-language pathologists make based on test results. By focusing on decisions, the framework requires critical evaluation of test use, rather than evaluating tests against sets of criteria. Adopting this framework has the potential for appreciable improvement in the way tests are used and valued across our profession. Conclusions Speech-language pathologists, test developers, and clients will benefit from improved evidence-based assessment practices. It is recommended that regulators, test developers, professional associations, universities, and researchers adopt the framework and endorse it as best practice moving forward. This viewpoint proposes a series of first steps toward supporting uptake of the framework into research and practice.
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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 | 0.456 | 0.549 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.008 |
| Science and technology studies | 0.008 | 0.082 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.008 | 0.013 |
| 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".