A how-to guide to aphasia services: celebrating Professor Linda Worrall’s contribution to the field
Bibliographic record
Abstract
Background: This article recognises Professor Linda Worrall’s contribution to aphasiology and discusses research themes which have grown from her work.Aims: To review, summarise, and discuss literature relating to four themes which have emerged from the work of Professor Worrall: (1) Research capacity building; (2) Implementation of research evidence in clinical practice; (3) Meaningful outcome measurement; and (4) Improvement of psychological and emotional outcomes.Main contribution: A review of the literature, with examples of practical applications.Conclusions: The work of Professor Worrall has greatly influenced the field of aphasia; her legacy is the research capacity she has built in Australia and around the world.
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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 | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.015 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".