Psychiatric emergency department visits during the coronavirus disease-2019 pandemic
Bibliographic record
Abstract
Background: Previous research has demonstrated the negative impact of the coronavirus disease-2019 (COVID-19) pandemic on mental health. Aims: To examine changes in the Chinese psychiatric emergency department (PED) visits for mental health crises that occurred during the pandemic. Methods: Before and during the COVID-19 pandemic, PED visit counts from the largest psychiatric hospital in China between 2018 and 2020 were investigated. Electronic medical records of 2020 PED visits were extracted during the COVID-19 pandemic period and compared for the same period of 2018 and 2019. Results: Overall, PED visits per year increased from 1,767 in 2018 to 2210 (an increase of 25.1%) in 2019 and 2,648 (an increase of 49.9%) in 2020. Compared with 2 years before the epidemic, during the COVID-19 pandemic, the proportion of PED visits among patients with stress disorders, sleep disorders, and anxiety disorders increased significantly. In terms of the distribution of demographic characteristics, age shows a younger trend, while the gender difference is not significant. Conclusion: These findings suggest that PED care-seeking increases during the COVID-19 pandemic, highlighting the need to integrate mental health services for patients with stress, sleep, anxiety, and obsessive-compulsive disorders during public health crises.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".