Narrative discourse of young and older brazilian adults associated with demographic factors/ Discurso narrativo de adultos jovens e idosos do brasil associado com fatores demográficos
Bibliographic record
Abstract
ABSTRACT Purpose: 1) Compare the discourse performance between young and older adults from the Brazilian Federal District (DF); 2) Compare the mean discourse performance of participants from the DF with the normative discourse of a population from a different region of the country; 3) Verify whether the variables age, educational level and socioeconomic status and scores on the cognitive, behavioral and functional screening tests were associated with discourse performance. Method: A total of 60 healthy volunteers from the DF, 30 older adults and 30 young adults, were selected. Participants were divided into two subgroups according to educational level: low education and high education. The four narrative discourse subtests of the Montreal Communication Evaluation Battery, Brazilian Portuguese version (MAC-BR) were applied to the study sample. Results: Discourse scores of the older adults were statistically higher than those of the young adults. The discourse scores in the high education group were also better than those in the low education group, with statistically significant difference observed in only one of the MAC-BR subtests. Discourse performance was associated with the sociodemographic variable and the scores on the cognitive and functional screening tests. The discourse performance of the DF sample differed from the national normative discourse with statistically significant difference. Conclusion: The discourse performance of older adults from the Brazilian Federal District differed from that of young adults from the same region, as well as from that of older adults from southern Brazil. Discourse performance was associated with several different variables.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".