Effects of training volunteers to converse with nursing home residents with aphasia
Bibliographic record
Abstract
Background: Nursing home residents with aphasia often experience social isolation. Providing trained conversation partners is one way to combat this problem, but evidence is needed for the effects of training conversation partners for persons with aphasia. The use of four college student volunteers was based on evidence for the benefits of intergenerational service‐learning programmes. Aims: The purpose of this study was to examine the effects of training four college student volunteers (SVs) to use multi‐modality communication with two nursing home residents with Broca's aphasia (RAs). Methods & Procedures: An ABA multiple baseline across subjects (SVs) and partners (RAs) design was used to examine the effects of the training programme in probe conversations. Each RA interacted with two SVs. Training consisted of five steps, with a criterion to move through each step of the programme, and to withdraw training. Thorough treatment fidelity procedures were used to ensure consistent training across subjects. Outcomes & Results: The SVs demonstrated marked increases in multi‐modality communication, with concomitant increases in RAs' comprehensibility. Sequential analyses revealed that multi‐modality communication is more likely than speech only to elicit RAs' comprehensible responses, with a stronger effect after training. Social validity ratings demonstrated that the changes in the quality of the conversations were clinically significant. Conclusions: This study revealed positive effects of training conversation partners of persons with aphasia to use multi‐modality communication. Intergenerational service‐learning programmes are one viable method to decrease social isolation and to increase opportunities for nursing home residents with aphasia to reveal their communicative competence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".