How clerks understand the requests of people living with aphasia in service encounters
Bibliographic record
Abstract
Aphasia often restricts participation. People living with aphasia (PLWA) engage in fewer activities, which leads to fewer interactions than before aphasia. Analyses of interactions with non-familiar people in activities of daily life could provide knowledge about how to integrate these situations in rehabilitation and facilitate ongoing PLWA participation post-rehabilitation. This qualitative study is the first to examine how PLWA make their requests understood in service encounters despite aphasia. Six people living with moderate or severe aphasia were video-recorded in situations of service encounters, e.g., pharmacies, specialised shops, restaurants, and others. We identified fifty-nine occurrences with one or several difficulties in the formulation of the request. They were examined, including the clerks' responses and ensuing interaction using multimodal conversation analysis. Results showed that PLWA used nonverbal communication within the physical environment and the context of the interaction to support verbal production. In the majority of situations, the clerks understood the request promptly. In other situations, they both collaborated to achieve a clear understanding of the request. Moreover, the findings attest to the competence of people living with moderate or severe aphasia in engaging in service encounters and add to the knowledge base about interaction and social participation in aphasia.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".