“Speaking for” behaviours in spouses of people with aphasia: A descriptive study of six couples in an interview situation
Bibliographic record
Abstract
Background: People with aphasia and their spouses frequently meet professionals to discuss health‐related issues. In this situation, which is often in an interview form, various strategies may be employed by spouses to facilitate communication. One of these strategies is “speaking for” the person with aphasia. Aims: (1) To identify the presence of “speaking for” behaviour, to measure the frequency of the spouses' “speaking for” and “rapid speaking for” behaviours, and to describe what preceded and followed these behaviours for all participating couples. (2) To describe each individual couple's patterns of “speaking for” in relationship to the members' perceptions of conversations before and after the onset of aphasia. Methods & Procedures: Six couples were studied in an interactive situation. Both the spouse with aphasia and the non‐aphasic spouse took turns being asked questions in a systematic way. Each member also participated individually in a semi‐structured interview aiming to obtain information on perceptions of communication before and since the aphasia. Outcomes & Results: Analysis of three‐way conversations revealed that all of the spouses without aphasia in this study used some “speaking for” behaviours. However, there was great variability in the frequency of the behaviours within couples. For some couples, “speaking for” the person with aphasia may reduce that person's ability or willingness to participate in conversations. The findings from the semi‐structured interviews suggest that “speaking for” a person with aphasia may be an integral behaviour for some couples that is consistent with pre‐stroke interaction patterns. Conclusions: It is important to consider the “speaking for” behaviour, the impact of this behaviour, and the pre‐stroke interaction pattern when helping couples adjust to the consequences of 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".