Assessment of Patients' Referral Patterns with Complaints ofSelf-harm and Aggression in the COVID-19 Era
Bibliographic record
Abstract
Background: Due to the high transmission rate of COVID-19, the high prevalence of the disease, the high mortality rate, and its effects on mental health, we aimed to assess the current status of psychiatric symptoms. Methods: In this observational study, we have assessed various psychiatric presentations and disorders before and after the COVID-19 pandemic within the same time limit. Data have been obtained from the psychiatric interview performed by an attending physician in psychiatry. Results: The following features have been observed after the pandemic: increased depressed mood, irritability, crime trend, physical violations, personality disorders along with improved family support, and decreased suicidal ideation. No significant difference has been observed in the rate of response to psychotherapy and psychiatric medications before and after the time of the pandemic. Conclusion: Increased physical threat and aggression, substance use, and symptoms of psychosis were more frequently observed in the time of the pandemic. The physical threat was mainly committed by younger patients with psychiatric illnesses.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".