CAREGIVER CONVERSATIONS WITH PERSONS WITH MILD COGNITIVE IMPAIRMENT, ALZHEIMER’S DISEASE, AND PARKINSON’S DISEASE
Bibliographic record
Abstract
Abstract This study describes conversations between persons living with mild cognitive impairment (MCI), Alzheimer’s Disease (AD), Parkinson’s Disease (PD) and their family caregivers, and the possible contributions of sensory loss. Conversations (N=130) between a participant (60% male) and a family caregiver (75% female; spouses) in the COMPASS-ND study were analyzed. Conversations were transcribed verbatim, segmented into communication units (c-units), and analyzed using the trouble-source repair (TSR) paradigm that includes analyses of communication breakdowns, repair initiators, and repair strategies. A one-way analysis of variance was conducted to evaluate between-group differences among sensory and communication variables. The AD group had poorer hearing than the MCI and PD groups. Both AD and MCI groups had poorer vision than the PD group. The MCI group used significantly more c-units than the AD and PD groups. The mean proportion of the TSR sequences was 13% across all groups, but with significantly more sequences in the AD and PD groups than the MCI group. Notably, the proportion of trouble sources created by caregivers was significantly greater for conversations in the AD group than the MCI or PD groups. The proportion of repair initiators used was significantly greater for the AD group than the MCI or PD groups. The intersection of cognition and sensory variables will be illustrated using three case studies. The findings highlight the active participation of persons living with AD and sensory loss in signalling misunderstandings created by caregivers. However, the interface between cognitive and sensory abilities influences the communication among both conversational partners.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".