“The most important thing is having patience, both of us.” Successful conversations from the perspective of people with aphasia and their primary conversation partners
Bibliographic record
Abstract
PURPOSE: Improving conversation between a person with aphasia (PWA) and their primary conversation partner (PCP) is a goal of aphasia therapy. However, there are few outcome measurements available that enable conversation success to be measured from the perspective of the target population. This study sought to define the construct of "conversation success" from the perspective of PWA and PCP in the development of a patient-reported outcome measure (PROM) of dyadic conversation. METHODS: = 19) participated in online focus groups using the nominal group technique. Participants responded to the question, "What makes your conversations successful with your communication partner?" and ranked the three most important items in terms of personal preference. Qualitative content analysis was used to analyse priorities across groups. RESULTS: In eight focus groups, 39 participants generated 190 items describing successful conversation. Five themes were identified: (1) working it out together, (2) having patience, (3) being familiar with your conversation partner, (4) considering the conversation environment, and (5) having a positive attitude and mindset. CONCLUSIONS: The participants conceptualised successful conversation in terms of behaviours, strategies, and feelings. These results will inform the development of a PROM for dyadic conversation 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".