Recovering memory: Sense‐making and recall strategies of individuals with mild cognitive impairment
Bibliographic record
Abstract
Abstract Cognitive impairment as it relates to making sense of information, or to communicating information needs, can range from mild disorientation and aphasia, to a complete loss of short‐term memory and use of language. While studies on semantic dementia (SD), for example, are evident in a range of health science disciplines, and social work, little research – particularly within the information science discipline – on categorization or classification strategies for sense‐making and recall among those with SD or related cognitive impairments, has been identified. This poster reports on a pilot study exploring “sorting” strategies for recalling everyday life experiences, and the effectiveness of multi‐modal tokens as context for association and reconstruction of participant scenarios. Preliminary to a larger study, participants diagnosed with early stage Alzheimer/Dementia (AD) responded to questions concerning personal everyday life events. After a delay of several weeks, they were asked to describe what they associated with representative non‐verbal tokens or cues. Recollections were compared with original scenarios to determine (1) whether and how the multi‐modal cues provided access to the initial recall of the everyday life event, (2) what additional scenarios, if any, were evoked, and (3) what associative links revealed, subsequently, about sense‐making and sorting strategies around memory recall. Understanding how these strategies assist with reconstructing episodic and semantic memories could inform the design of life history retrieval systems for reinforcing or recovering intact memory.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".