Perfetti method: a sustainable approach to cognitive and functional enhancement in intellectual disabilities
Bibliographic record
Abstract
Background: As for the functional and cognitive capacities of people with intellectual disabilities, there has been evidence of a deficit in activities of daily living such as eating, getting dressed, going to the bathroom, moving around, walking, going up and down steps. And as for the cognitive area, functions such as visuospatial, attention, memory, concentration and language were evaluated. A correct treatment has not been given to these areas so they cannot develop a better quality of life. Objective: To apply the Perfetti Method for functional and cognitive abilities improvement in patients with intellectual disabilities. Methods: The research is an observational study with a quantitative approach and with 20 participants. An initial assessment was made and after 12 weeks of intervention with the Perfetti Method a final assessment was carried out. Sociodemographic characteristics, Barthel index assessment scales and the Montreal Cognitive Assessment (MoCA)-Test were used. Results: After 12 weeks of applying the Perfetti Method Guide, it significantly improved functional and cognitive abilities and consequently the participants´ quality of life. The application of the T student test, for related samples, established a relation between the Barthel Index and the Montreal Cognitive Assessment (MoCA)-test, with a significance of (p<0.001); Conclusions: The Application of the Perfetti Method is effective to improve the functional and cognitive capacities of people with intellectual disabilities and their quality of life.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".