Faculty Opinions recommendation of Social cognitive training for schizophrenia: a meta-analytic investigation of controlled research.
Bibliographic record
Abstract
A wealth of evidence has revealed that deficits in social cognitive skills (including facial affect recognition (FAR), social cue perception, Theory of Mind (ToM), and attributional style) are evident in schizophrenia and are linked to a variety of domains of functional outcome. In light of these associations, a growing number of studies have attempted to ameliorate these deficits as a means of improving outcome in the disorder through the use of structured behavioral training. This study used quantitative methods of meta-analysis to assess the efficacy of behavioral training programs designed to improve social cognitive function. A total of 19 studies consisting of 692 clients were aggregated from relevant databases. Outcome measures were organized according to whether they were social cognitive tests proximal to the intervention or whether they represented measures of treatment generalization (symptoms, observer-rated community, and institutional function). With respect to social cognitive measures, weighted effect-size analysis revealed that there were moderate-large effects of social cognitive training procedures on FAR (identification, d = 0.71 and discrimination, d = 1.01) and small-moderate effects of training on ToM (d = 0.46), while effects on social cue perception and attributional style were not significant. For measures of generalization, weighted effect-size analysis revealed that there were moderate-large effect on total symptoms (d = 0.68) and observer-rated community and institutional function (d = 0.78). Effects of social cognitive training programs on positive and negative symptoms of schizophrenia were nonsignificant. Moderating variables and implications for future research and treatment development are discussed. PMID: 21525166 Funding information This work was supported by: NIMH NIH HHS, United States Grant ID: K08 MH-69888
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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 | 0.026 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.003 |
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".