Discourse Comprehension Test Performance of Elders With Dementia of the Alzheimer Type
Bibliographic record
Abstract
Spoken language comprehension, including comprehension for inferential material in narrative discourse, is diminished in dementia of the Alzheimer type (DAT). There are, however, no empirical data concerning comprehension by adults with DAT of main ideas versus details in narratives. Evidence from other groups with and without brain damage has shown that comprehension for main ideas is relatively better than for details and that comprehension for stated material is relatively better than comprehension for inferential material. Participants in the present investigation were 24 older adults, 8 with early-stage DAT (EDAT), 8 with middle-stage DAT (MDAT), and 8 with no brain damage (NBD). Selected narratives and associated sets of yes-no questions from the Discourse Comprehension Test (DCT) (Brookshire and Nicholas, 1993) were presented on videotape. Participants with EDAT and MDAT had significantly poorer overall comprehension of DCT narratives than did those in the NBD group (p < .0001), but they did not differ significantly from each other. Responses to DCT narratives by participants in the NBD, EDAT, and MDAT groups followed the same pattern of relatively better comprehension for main ideas than for details and relatively better comprehension for stated than for implied information. Working memory and episodic memory were shown to be significantly associated with DCT overall scores. Together, these findings suggest that although overall narrative comprehension is diminished in those with DAT, individuals appear to retain a mental representation for narratives that facilitates better comprehension of main ideas than of details as well as better comprehension of stated information than of implied information. This interpretation is consistent with schema-based accounts of narrative comprehension.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".