Speaking of yourself: A meta-analysis of 80 years of research on pronoun use in schizophrenia
Bibliographic record
Abstract
People with schizophrenia experience significant language disturbances that profoundly affect their everyday social interactions. Given its relevance to the referential function of language, aberrations in pronoun use are of particular interest in the study of schizophrenia. This systematic review and meta-analysis, adhering to PRISMA guidelines, examines the frequency of pronoun use in schizophrenia. PubMed, PsycINFO, Scopus, Google Scholar, and Web of Science were searched up to May 1, 2024. All studies analyzing pronoun frequency in various spoken language contexts in schizophrenia were included. Bias was assessed using a modified Newcastle-Ottawa Scale. A Bayesian meta-analysis with model averaging estimated effect sizes and moderating factors. 13 studies with n = 917 unique participants and 13 case-control contrasts were included. 37.9 % of patient samples were women, with a weighted mean (SD) age of 34.45 (9.72) years. 53.85 % of the studies were in languages other than English. We report a medium-sized effect for first-person pronoun impairment in schizophrenia (model-averaged d = 0.89, 95 % CrI (0.44, 1.33)). There was significant heterogeneity moderated by age. Evidence for publication bias was weak, with a strong support for first-person pronoun impairment after accounting for bias and heterogeneity. There was a small reduction of inter-individual variability in first-person pronoun use in patients compared to healthy controls (lnCVR = -0.12, 95 % CrI [-0.35, -0.13]). While all pronoun use was also high in patients, this was not robust due to heterogeneity and publication bias. Individuals with schizophrenia excessively use first-person pronouns. This may be a marker of a disturbed sense of self in this illness.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.051 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".