Mental representations of daily activities throughout the course of dementia
Bibliographic record
Abstract
BACKGROUND: Since dementia is a result of cognitive rather than physical impairment, cognitive aspects are important for care planning. This mixed-model study aims to understand how the loss of cognitive functioning affects mental representations of daily activities. METHODS: Mental representations were assessed via the script generation task of daily activities (grocery shopping, dentist appointment, doing laundry, leaving the house, car accident) and a qualitative semi-structured interview from 25 people (age (mean: 67.64; SD: 23.625), gender (f: 14 (56%); m: 17 (68%)). Cognitive status was assessed via the Montreal Cognitive Assessment. RESULTS: Mental representations of daily activities loose content and get inaccurate throughout the disease (i.e. number of actions, abstractions, unemotional content) with poorer cognitive status. People with mild dementia report the most strategies and extend their mental representation by including strategies to circumvent experienced problems. Overall, mental representations of daily activities seem to be largely intact throughout the course of dementia (i.e. sequencing, personalisations, intrusions, examples, emotional content). CONCLUSION: This study outlines that even though the content of mental representations decreases with dementia, the mental representations themselves remain in good order. Performance of daily activities throughout dementia may be hampered by the loss of content of the generated actions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".