2336 – Deficit Institutionalization: Cognitive Screening Of Institutionalized Elderlies
Bibliographic record
Abstract
Our aim is to discuss the use of cognitive assessment tools in generational cohorts with high rates of illiteracy. The scales used to evaluate the cognitive impairment were the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Three items were added to assess the domain of language. The sample included 224 institutionalized elderly from municipality of Miranda do Corvo - Portugal, mean age of 83.76 years (± 7.29), 179 were women (79.9%), and 93 (41.5%) had no education. The assessment took place between December 2011 and May 2012 and showed that people had high levels of cognitive impairment. The difference was proved to be statistically significant regardless the considered age cuts of 65, 65–74 or 75. Through the MMSE, we found that 55 of the 144 respondents (38.2%) had cognitive impairment. If we add the other elderlies diagnosed with dementia, the prevalence rises to 60.3%. The evaluation conducted by MoCA showed that 140 (97.2%) of the elderly had cognitive impairment. If we add to these the remaining respondents diagnosed with dementia, this percentage rises to 98.2%. The results were related to the sociodemographic characteristics of this generational cohort and attested the prevalence of cognitive impairment in institutions for elderly people. Thus, we propose that institutions implement cognitive stimulation programs for the maintenance and improvement of cognitive abilities of the elderly, and we methodologically discuss the use of these scales to the measure of the cognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".