Cognitive evaluation and rehabilitation in high‐ and low‐income countries
Bibliographic record
Abstract
Starting from her own personal experience, in the First Part of the article, the author reconstructs how the specialized sectors of cognitive evaluation and rehabilitation evolved in Western countries (Europe, the United States, Canada, and Australia, in particular) during the second half of the last century and the first decades of this century. In the Second Part, she describes her personal experience in setting up a rehabilitation centre dedicated to traumatic brain-injured subjects and her commitment to international cooperation (Bolivia, Rwanda, Myanmar, Tanzania) in the field of cognitive evaluation and rehabilitation in favour of people with congenital and acquired cerebral pathology, especially in the paediatric age, since there is an almost total lack of diagnostic, but above all, rehabilitative procedures for cognitive functions in low-middle income countries. In the Third Part of the article, the author carries out an extensive review of the international literature on the differences in access to cognitive diagnostic evaluation and cognitive rehabilitation in middle- and low-income countries - but not only - underlining the urgent need to launch a major international collaborative effort to reduce and eliminate these discrepancies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".