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 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 | 0.037 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".