Making Assessment Real: Audrey Holland's Contributions to the Assessment of Aphasia and Cognitive-Communication Disorders in Clinical and Research Settings
Bibliographic record
Abstract
For half a century, Dr. Audrey Holland investigated, developed, and implemented ways to extend the assessment of adult language and cognitive-communication disorders beyond traditional impairment-based approaches. This article summarizes Dr. Holland's many groundbreaking contributions to assessment practices by describing and exemplifying major conceptual and measurement innovations that have emerged from her research of both formal and informal assessment techniques. Dr. Holland's assessment contributions encompass the development of many widely used measures of functional communication, discourse, and cognitive-communication abilities. She also contributed to the development of assessment principles that have become part of best-practice standards of care. Some of her most significant contributions include: Drawing attention to assessment within authentic functional contexts; highlighting connections between language, communication, related cognitive abilities, and broader aspects of health including quality of life; raising psychometric standards; and emphasizing the value of implementing multiple person-centered measurement techniques spanning formal and informal as well as quantitative and qualitative approaches. Dr. Holland's career-long commitment and contributions to developing more meaningful and authentic assessment practices have transformed our field and substantively elevated the quality of care and services that we are able to provide to all persons who are impacted by language and cognitive-communication disorders.
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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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.018 |
| Insufficient payload (model declined to judge) | 0.001 | 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".