Predictors of Depressive Symptoms in Individual with First–episode Schizophrenia
Bibliographic record
Abstract
Purpose: To examine the predictive power of predictor variables: Cognitive Insight, Medication Adherences, and Social Support on depressive symptoms in individual with first-episode schizophrenia. Design: Descriptive correlational study. Methods: The sample consisted of 77 patients with first-episode schizophrenia who had a duration of illness of no more than five years since first diagnosis. The sample was selected by convenience sampling according to inclusion and exclusion criteria. The instruments included 1) The Beck Cognitive Insight Scale 2) The Medication Adherence Report Scale, 3) The Medical outcomes Study Social Support Survey, and 4) The Thai version of Calgary Depression Scale for Schizophrenia were analyzed using descriptive statistics and Multiple Linear Regression. Main findings: The findings revealed that 75.32 % of the sample had depressive symptoms. Cognitive insight was found to be positively related to depressive symptoms to a high degree (r = .710, p 0.05). These three factors altogether could 58.4 % of the variance in depressive symptoms in patient with firstepisodes schizophrenia (p 0.05) และพบวา ทงสามตวแปรสามารถรวมกนอธบายความแปรปรวนของอาการซมเศราในผปวยจตเภททมอาการทางจตครงแรกไดรอยละ 58.4 (p < .001) สรปและขอเสนอแนะ: ขอเสนอแนะจากผลการวจยคอ พยาบาลจตเวชควรสงเสรมใหผปวยมความเขาใจการเจบปวยทางจตใหถกตอง ในขณะเดยวกนควรสงเสรมใหผปวยไดรบการสนบสนนทางสงคมจากครอบครว เพอปองกนและลดการเกดอาการซมเศราในผปวยโรคจตเภททมอาการทางจตครงแรก คำสำคญ: ความเขาใจการเจบปวยทางจต อาการซมเศรา การสนบสนนจากครอบครว การใหความรวมมอในการรกษาดวยยา โรคจตเภท
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".