The COVID-19 pandemic in Russia: Effects on clinicians and mental health services
Bibliographic record
Abstract
Since COVID-19 was declared a pandemic by the World Health Organization in March 2020, it has had different infection rates across the world. Russia had one of the largest numbers of infected cases during 2020, but with a lower overall fatality rate. Nevertheless, as in other countries, clinical practice within the mental health care system has faced many stresses and challenges. This concerned the need to organize a treatment of COVID-19 in psychiatric hospitals, as well as a transformation of outpatient care, including psychotherapy, which has largely switched to a remote format. To better understand the effects of the pandemic on mental health professionals, a large-scale study has been implemented through the Global Clinical Practice Network, one of the largest professional communities, which includes 969 members from Russia. The study assessed how COVID-19 affected clinical practice and well-being of clinicians. The first of three surveys was launched in June 2020, in six languages including Russian. Over 2,500 global mental health professionals participated in the study, including 205 clinicians from Russia. Current work circumstances, work-related stressors, and use of telehealth were evaluated. In Russia, the data collection period was characterized by generally improvement in the overall pandemic situation. Results to be presented include the proportion of clinicians that continued working, what kinds of services they provided, their well-being strategies, telehealth modalities and areas in which they had particular concerns about assessment, treatment, or monitoring of patients with mental disorders using remote technologies. Disclosure No significant relationships.
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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 | 0.003 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".