Bibliographic record
Abstract
Although a bilingual advantage has been described for neurodegenerative disease in general, it is not known whether such an advantage could accrue to individuals suffering from schizophrenia, since language networks are known to be disrupted in this condition. The aim of this minireview was to scan the existing literature to determine: (1) whether individuals with schizophrenia are able to learn a second language as adults; (2) whether clinical assessment, both for the purpose of accurate diagnosis and for the prediction of treatment response, should be carried out in both languages in bilinguals with schizophrenia; (3) whether psychotherapy in schizophrenia is affected by bilingualism; and (4) whether speaking a second language improves outcome in schizophrenia. The literature to date is too sparse to make definitive statements, but: (1) individuals with schizophrenia appear to be capable of learning a new languages as adults; and (2) it is possible that teaching a foreign language may serve as a form of cognitive rehabilitation for this condition. This literature review recommends research into the effects of bilingualism on the outcome of schizophrenia. Included in this review is a retrospective pilot study conducted in Canada, which suggests that employment opportunities for patients with schizophrenia are improved when they speak more than one language. This is important to note because employment is generally problematic in the context of schizophrenia while, at the same time, the ability to obtain work contributes significantly to quality of life.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".