Leading the Way in Dementia Care: Embracing the Whole Person
Bibliographic record
Abstract
Audrey Holland was a leading innovator and speech-language pathologist (SLP) in adult neurological communication disabilities for over five decades. She was a pioneer in the involvement of SLPs with people with dementia, inspiring both knowledge development and clinical practice regarding language, functional communication, and quality of life in persons living with dementia. Dr. Holland was also an extraordinary mentor who has impacted many generations of researchers and clinicians. Here, four researchers in the area of dementia and communication discuss the lessons they learned from Dr. Holland that fundamentally shaped their careers and the field of dementia and speech-language pathology. Lessons learned include the following: (1) do not be afraid to stand out when you have a novel idea that will help people; (2) look for strengths to support functional communication; (3) use communication strategies to support identity, quality of life, and self-determination in adults with acquired communication disabilities, including those with dementia; (4) shift from pathologizing to coaching; and (5) challenge the status quo. This article concludes by discussing Dr. Holland's lasting legacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".