Resilience Improves Neurocognition and Treatment Outcomes in Schizophrenia: A Hypothesis
Bibliographic record
Abstract
There has been a revolutionary advance in the treatment and management of schizophrenia from a clinical aspect yet the social and functional outcomes remain poor. Cognitive function is impaired in schizophrenia and shows various domains of dysfunction like verbal memory, processing speed and working memory. It is also known to be a factor associated with poor outcome in schizophrenia. Resilience is a new concept psychobiological concept which is defined as individual’s ability to adapt swiftly to adverse life events and bounces back to normalcy. Resilience has genetic, neurobiological, neurochemical and psychological underpinnings. It is the ability to effectively deal with psychosocial stressors and appears to be one of the many factors associated with favourable outcomes in schizophrenia. Besides several neurobiological abnormalities associated with resilience, neucognitive functions are of particular interest. Persistent psychosocial stressors also lead to significant neurobiological changes which may be synergetic to poor outcome due to cognitive changes. Though there has been extensive research in the field of cognitive function in schizophrenia, the trajectory of its pathway of poor outcome remains undetermined. Resilience being a protective factor may be one of the psychobiological functions which modulate the effect of neurocognition on the outcome of schizophrenia. There has been some success with interventions aimed at improving cognitive function in schizophrenia whether pharmacological or non pharmacological. In this paper, we discuss a hypothesis that resilience may be a “linkage” between cognition and outcome. There is a need for interventions aimed at increasing resilience in patients with schizophrenia and we hypothesize giving evidence that this may in turn improve outcome and neurocognitive functioning in schizophrenia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".