Reliability of the Conversational Interaction Coding Form When Applied to Natural Conversation of Individuals With Aphasia
Bibliographic record
Abstract
Purpose: Development of valid and reliable outcome tools to document social approaches to aphasia therapy and to determine best practice is imperative. The aim of this study is to determine whether the Conversational Interaction Coding Form (CICF; Pimentel & Algeo, 2009) can be applied reliably to the natural conversation of individuals with aphasia in a group setting. Method: Eleven graduate students participated in this study. During a 90-minute training session, participants reviewed and practiced coding with the CICF. Then participants independently completed the CICF using video recordings of individuals with non-fluent and fluent aphasia participating in an aphasia group. Interobserver reliability was computed using matrices representative of the point-to-point agreement or disagreement between each participant's coding and the authors' coding for each measure. Interobserver reliability was defined as 80% or better agreement for each measure. Results: On the whole, the CICF was not applied reliably to the natural conversation of individuals with aphasia in a group setting. Conclusion: In an extensive review of the turns that had high disagreement across participants, the poor reliability was attributed to inadequate rules and definitions and inexperienced coders. Further research is needed to improve the reliability of this potentially useful clinical tool.
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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 | 0.063 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".