Generalization in Aphasia Treatment: A Tutorial for Speech-Language Pathologists
Bibliographic record
Abstract
PURPOSE: Generalization has been defined and instantiated in a variety of ways over the last half-century, and this lack of consistency has created challenges for speech-language pathologists to plan for, implement, and measure generalization in aphasia treatment protocols. This tutorial provides an overview of generalization with a focus on how it relates to aphasia intervention, including a synthesis of existing principles of generalization and examples of how these can be embedded in approaches to aphasia treatment in clinical and research settings. METHOD: Three articles collectively listing 20 principles of generalization formed the foundation for this tutorial. The seminal work of Stokes and Baer (1977) focused attention on generalization in behavioral change following treatment. Two aphasia-specific resources identified principles of generalization in relation to aphasia treatment (Coppens & Patterson, 2018; Thompson, 1989). A selective literature review was conducted to identify evidence-based examples of each of these 20 principles from the extant literature. RESULTS: Five principles of generalization were synthesized from the original list of 20. Each principle was supported by studies drawn from the aphasia treatment literature to exemplify its application. CONCLUSIONS: Generalization is an essential aspect of meaningful aphasia intervention. Successful generalization requires the same dedication to strategic planning and outcome measurement as the direct training aspect of intervention. Although not all people with aphasia are likely to benefit equally from each of the principles reviewed herein, our synthesis provides information to consider for maximizing generalization of aphasia treatment outcomes. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24714399.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".