M119. Linguistic Content in Schizophrenia and Bipolar Disorder: Relationships With Cognition and Social Functioning
Bibliographic record
Abstract
Background: Individuals with schizophrenia and bipolar disorder suffer from impairments in social functioning. People with schizophrenia tend to use fewer words overall when speaking compared healthy controls, and fewer words with positive emotional valence, yet it is unknown how this linguistic structure compares to the structure in bipolar disorder and how linguistic structure is related to social functioning. Methods: Thirty-nine individuals with bipolar disorder and 42 individuals with schizophrenia were randomly selected from a larger study. All participants were audio recorded while they completed the Social Skills Performance Assessment (SSPA), which comprises role plays of making plans with a friend, greeting a new neighbor, and negotiating with a landlord. Recordings were transcribed and processed using the Linguistic Inquiry and Word Count (LIWC2015). Participants also completed a standard cognitive assessment battery, the Positive and Negative Syndrome Scale (PANSS), Beck Depression Inventory (BDI), and the Specific Levels of Functioning rating scale (SLOF). Results: Compared to manualized norms, individuals with schizophrenia and bipolar disorder used significantly fewer complex words, d = 0.53, fewer positive emotion words, d = 0.66, and more nonfluencies and filler words, d = 1.58. There was no difference in the number of negative emotion words used. Participants with bipolar/schizophrenia used significantly fewer first person words, d = 1.98, and more second person words, d = 1.90 than in normative data. Between diagnostic groups, people with schizophrenia used significantly fewer words, d = 0.86, and fewer negative emotion words, d = 0.61 than people with bipolar disorder. Total number of words used and number of negative emotion words were related to both the cognitive composite score and real world interpersonal functioning (rs = .33–.55), however linguistic variables contributed small, nonsignificant variance to interpersonal functioning after accounting for clinical symptoms. Conclusion: Generally, people with schizophrenia and bipolar disorder communicated similarly; however, both groups exhibited substantial differences in linguistic structure compared to normative data. Treatments focused on helping people use more positively valenced words and normalizing the number of second person references compared to first person references may help improve interpersonal functioning for people with severe mental 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".