Tracking changes in conversation during and after communication partner training: an exploratory study
Bibliographic record
Abstract
Background: While communication partner training (CPT) is recommended in the treatment of aphasia, more research on the long-term effect of CPT is needed to enhance the knowledge base about its effects.Aim: To measure the effects both during training and maintenance phases of a CPT offered to a spouse of a man with aphasia by using quantitative and qualitative procedures.Method: One couple participated in 8 sessions of CPT. Conversations were recorded at baseline, throughout the intervention and three months post-intervention. The intervention targeted the type of questions asked by the spouse and the use of extended pauses in conversation. We defined these behaviors and counted their occurrences using a software. Visual analysis and standard deviation band analysis were performed. Also, the spouse participated in a semi-structured interview before and after intervention, which was analysed qualitatively.Outcomes & Results: Positive changes were observed during intervention – more open-ended questions were produced, and the number of pauses increased – but these improvements were not maintained three months later. The spouse reported that CPT had changed their daily communication and that it provided support for her.Conclusions & Implications: The quantitative results gave precise and specific information on how the spouse responded to CPT overtime and that could lead to suggestions regarding the content of the intervention in order to promote maintenance in future studies. The qualitative analysis reflected the broader impact of CPT on the couple’s communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".