Characterizing communication in dementia family caregiving: Identifying breakdown and repairs using the trouble source repair (TSR) framework via video observations
Bibliographic record
Abstract
Abstract Background Families who provide care at home for people living with dementia (PLWD) are an integral part of dementia care worldwide and improving in‐home care is crucial. Communication is fundamental for daily care and communication breakdown presents additional stress to the caregiver‐PLWD dyad. In order to identify effective strategies that will minimize communication breakdown, we adapted the Trouble Source Repair (TSR) framework, a linguistic tool, to create a second‐by‐second behavioral coding scheme for video observations. This pilot study used the adapted TSR coding scheme to reliability identify communication breakdown and repair. Method This pilot study analyzed videos from a randomized controlled trial of a family caregiver telehealth intervention (FamTechCare). The videos were created by caregivers in their home, providing observations of natural caregiver‐PLWD interactions across a variety of daily care activities. Operational definitions from the TSR were adapted to create a coding scheme (Table 1). Video data collection and analysis were completed using Noldus software. Two independent coders obtained reliability. Result The video sample includes 51 dyads with 201 videos averaging 5.8 minutes in length. Caregivers were primarily female (80%), mean age 65 years, and were mostly a spouse (69%) while the PLWDs were primarily male (55%) with moderate to severe dementia (64.7%). The mean interrater reliability was excellent; Kappa = .93. Preliminary descriptive results (n=106) indicate 53.7% of the dyad communication was interactive, 8% was communication breakdown, and the remainder was silence or talking to others. During a typical breakdown sequence, the caregiver’s communication creates the breakdown (Trouble Source, 67%), the PLWD indicates not understanding (Flag, 71%) and the caregiver initiates the Repair (72%). Breakdowns were successfully resolved 71% of the time. Types of trouble sources, flags, and repairs can be seen in Table 2. Conclusion The results support the use of the adapted TSR framework to reliability identify communication breakdowns via video observation. Future analyses will identify the most effective strategies to prevent and repair communication breakdown as well identify how strategies varies by dementia stage, diagnosis, and dyad characteristics.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".