Naming gains and within-intervention progression following semantic feature analysis (SFA) and phonological components analysis (PCA) in adults with chronic post-stroke aphasia
Bibliographic record
Abstract
Background: Up to 60% of people with aphasia experience persistent word-finding difficulties into the chronic stage, starting six months after the stroke. Semantic Feature Analysis (SFA) and Phonological Components Analysis (PCA) are two common word-finding interventions that use the generation of semantic features for SFA (e.g. category) and phonological features for PCA (e.g. first sound) to improve naming. Despite inconsistent support for the generalization to untreated items, studies have shown improvements on probe word naming for treated items. However, research concerning within-intervention effects and generalization to alternative contexts has been limited.Aim: This study investigated the effect of treatment for SFA and PCA probe word naming as well as their within-intervention effects in four individuals with chronic post-stroke aphasia.Methods & Procedures: Baseline and follow-up measures included standardized assessments and image naming tasks. The image naming task was used to generate three lists: an SFA treated list, a PCA treated list, and an untreated list. One SFA session and one PCA session per week were then provided concurrently to each participant for a period of six weeks.Outcomes & Results: While only one participant experienced significant gains on treated probe word naming, these gains were maintained four weeks after the intervention. Exploratory results suggested that effects could transfer to two types of generalization items: different pictures of the same items and pictures of items shown in a natural context. Furthermore, while generalization to untreated items did not reach significance for any participant, some generalization of gains to standardized assessments was observed. Although rarely equivalent for SFA and PCA interventions, all participants also experienced some degree of within-intervention improvement over the progression of sessions. These improvements included a reduction in the number of forced choices required for feature generation and/or a reduction in the number of words never named during intervention sessions.Conclusion: The results support additional avenues of investigation for SFA and PCA therapies for individuals with post-stroke aphasia, namely within intervention effects and the potential for generalization to different contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".