Qualitative study of affective identities in dementia patients for the design of cognitive assistive technologies
Bibliographic record
Abstract
Our overall aim is to develop an emotionally intelligent cognitive assistant (ICA) to help older adults with Alzheimer's disease (AD) to complete activities of daily living more independently. For improved adoption, such a system should take into account how individuals feel about who they are. This paper investigates different affective identities found in older care home residents with AD, leading to a computational characterization of these aspects and, thus, tailored prompts to each specific individual's identity in a way that potentially ensures smoother and more effective uptake and response. We report on a set of qualitative interviews with 12 older adult care home residents and caregivers. The interview covered life domains (family, origin, occupation, etc.), and feelings related to the ICA. All interviews were transcribed and analyzed to extract a set of affective identities, coded according to the social-psychological principles of affect control theory (ACT). Preliminary results show that a set of identities can be extracted for each participant (e.g. father, husband). Furthermore, our results provide support for the proposition that, while identities grounded in memories fade as a person loses their memory, habitual aspects of identity that reflect the overall "persona" may persist longer, even without situational context.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".