Climate change anxiety and symptom severity in individuals with schizophrenia across seasonal variations: a prospective cohort study
Bibliographic record
Abstract
Abstract Background Schizophrenia demonstrates complex interactions with environmental factors, including climate change. This study aimed to investigate the relationship between climate change anxiety and symptoms severity among individual with schizophrenia across seasonal variations and it determines. A cohort study was conducted at Assiut University's Psychiatry Hospital, involving 40 individual with schizophrenia and 40 healthy controls. Participants were assessed using multiple tools including the Personality Inventory for DSM-5 (PID-5), Montreal Cognitive Assessment (MOCA), Climate Change Anxiety Scale (CCAS), Symptom Checklist-90-Revised (SCL-90-R), and Positive and Negative Syndrome Scale (PANSS). Data collection spanned a full annual cycle to capture seasonal variations. Results The schizophrenia group showed elevated scores across all personality subscales and lower cognitive function scores than other group. In addition, schizophrenia group exhibited significantly higher climate change anxiety scores compared to controls, with pronounced seasonal variations. Summer presented the highest mean scores for positive symptoms (16.4 ± 5.935), negative symptoms (20.45 ± 5.033), and general psychopathology (39.28 ± 9.597). Medical comorbidity emerged as a significant predictor of climate change anxiety in autumn and winter, while negative symptoms predicted anxiety during winter and spring periods. Conclusions Schizophrenia group experience significant seasonal fluctuations in climate change anxiety, and symptoms, particularly during summer.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".