Exploration of a quantitative method for measuring behaviors in conversation
Bibliographic record
Abstract
Background: The literature on communication partner training (CPT) includes mainly studies with a small number of participants, because methods to measure changes in conversation pose practical challenges limiting the analysis of large samples.Aim: The aim of this study was to explore a quantitative procedure that would allow one to measure specific behavioral changes occurring in conversational exchanges involving a person with aphasia and a partner.Methods & Procedures: Forty-three problem-solving situations presented visually as well as with a simple written explanation were created to elicit conversation. In order to test the situations and develop further a procedure, we used data from a spouse of a man with aphasia during CPT delivered in a clinical setting. We developed specific definitions related to conversational behaviors targeted in the CPT. These defined behaviors were analyzed using a transcription-less method and an annotation software in the couple’s 39 conversation samples collected before, throughout, and 3-months post CPT. Reliability data were collected.Outcomes & Results: The procedure enabled us to create a protocol with two types of conversational situations and reliable definitions for measurement of conversational behaviors in a timely fashion. Pilot data of the measures are provided.Conclusions: It is expected that the method presented in this pilot study may be used to document the outcomes of CPT. It could be used with single-subject designs that require repeated measures and multiple group designs that require comparable data over large samples. It provides a method of data collection and analysis to better evaluate the effects of conversation-based treatments such as CPT.
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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 | 0.060 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".