Developing Behavioral Coding to Understand Family Communication Breakdown in Dementia Care
Bibliographic record
Abstract
Communication breakdown is a challenge for family caregivers of persons living with dementia. We adapted established theory and scales for computer-assisted behavioral coding to characterize caregiver communication for a secondary analysis. We developed verbal, nonverbal, and breakdown coding schemes and established reliability (κ > .85). Within the 221 family caregiving videos analyzed, 55% of exchanges were interactive, 30% were silence, 4% consisted of talking to self or others, and 8% included a breakdown. An average of 2.4 ( SD = 1.9) breakdowns occurred per observation and were successfully resolved 85% of the time, with 31% being resolved most successfully following only one flag and repair strategy. Caregivers were the primary speakers (67%); their communication preceded most breakdown (65%), and they primarily initiated the repairs after a breakdown (70%). Common repair strategies included clarifications (31%), asking questions (24%), and repeating information (24%). Associations between communication strategies and repair success will provide evidence for caregiver training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".