Entre les familles et les services formels, le désir des aînés ayant besoin d'aide
Bibliographic record
Abstract
Sign comprehension is critical for effective driving, responses to warnings, and way-finding. Signs that are poorly comprehended by older people increase accident risk and may compromise independence. This study sought to determine whether iconic sign comprehension suffers in healthy aging and in the presence of cognitive impairment. Additionally, we examined whether the addition of text to iconic signage would increase comprehension in older adults. In Experiment 1, young adults, healthy older adults, and older adults with varying levels of cognitive impairment were asked the meaning of 65 signs used for driving, warnings, and way-finding. Healthy older adults were generally good at sign comprehension but had difficulty with way-finding signs. Older adults with cognitive impairment had poorer sign comprehension overall and particular difficulty with way-finding icons and signs that had icons only. In Experiment 2, healthy older adults were asked the meaning of signs containing icons only, or icons and text. A significant improvement in comprehension was found when text was added. An important implication of this work is that the assessment of sign comprehension needs to involve a broad and heterogeneous sample of older adults reflecting the range of perceptual and cognitive abilities represented in the population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".