Descriptive statistics on measurement tools.
Bibliographic record
Abstract
<div><p>Insight is a continuous and multidimensional phenomenon, including awareness of having an illness, the presence of symptoms and accurate symptom attribution, the need for treatment, and the consequences of treatment. Good insight into illness is associated with better adherence to treatment, better cognitive, psychosocial, and vocational functioning along with less symptom severity, decreased relapses, and hospitalizations. Several tools are used for insight evaluation. We recruited 90 patients diagnosed with schizophrenia and analyzed the forms of 58 patients. The patients completed the VAGUS-SR (self-rated), Beck Cognitive Insight Scale, Knowledge About Schizophrenia Questionnaire, and Multidimensional Scale of Perceived Social Support (MSPSS). Clinicians performed a mental status examination and completed the Positive and Negative Syndrome Scale, Schedule for the Assessment of Insight, VAGUS-CR (clinician-rated), Calgary Depression Scale for Schizophrenia, and Clinical Global Impressions. We found that the level of insight evaluated using the VAGUS forms increased with knowledge regarding schizophrenia. Upon investigating the relationship between perceived social support and insight, we identified a relationship between VAGUS-CR and only significant other subscales of MSPSS, and between one of the VAGUS-SR scale sub-dimensions and significant other and total scores of MSPSS. Our findings also suggest that the VAGUS-SR and VAGUS-CR scales can be used to evaluate insight in Turkish populations. The positive relationship between perceived social support and insight emphasizes the importance of increasing social support through interventions aimed at improving insight. Our data also highlighted the value of psychoeducational studies in this patient group. Considering the multidimensional effects of insight on patients with schizophrenia, it would be beneficial to use scales such as VAGUS, which allow the insights of individuals to be evaluated in detail by both the clinician and the patient.</p></div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.015 |
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; both teacher heads agree on what is shown here.
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".