Training Volunteers as Conversation Partners Using "Supported Conversation for Adults With Aphasia" (SCA)
Bibliographic record
Abstract
This article reports the development and evaluation of a new intervention termed "Supported Conversation for Adults with Aphasia" (SCA). The approach is based on the idea that the inherent competence of people with aphasia can be revealed through the skill of a conversation partner. The intervention approach was developed at a community-based aphasia center where volunteers interact with individuals with chronic aphasia and their families. The experimental study was designed to test whether training improves the conversational skills of volunteers, and, if so, whether the improvements affect the communication of their conversation partners with aphasia. Twenty volunteers received SCA training, and 20 control volunteers were merely exposed to people with aphasia. Comparisons between the groups' scores on a Measure of Supported Conversation for Adults with Aphasia provide support for the efficacy of SCA. Trained volunteers scored significantly higher than untrained volunteers on ratings of acknowledging competence [F(1, 36) = 19. 1, p < .001] and revealing competence [F(1, 36) = 159.0, p < .001] of their partners with aphasia. The training also produced a positive change in ratings of social [F(1, 36) = 5.7, p < .023] and message exchange skills [F(1, 36) = 17.6, p < .001 ] of individuals with aphasia, even though these individuals did not participate in the training. Implications for the treatment of aphasia and an argument for a social model of intervention are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".