The impact of COVID-19 on clinical practice and well-being of global mental health professionals
Bibliographic record
Abstract
Some of the most direct and brutal effects of the COVID-19 pandemic are experienced by health care professionals who are working in demanding environments while having to deal with their own fears of infection and mortality. To assess the impact of COVID-19 on the practice and well-being of global mental health professionals, we designed a three-part, longitudinal, internet-based study. Here we present data from part 1, implemented in June-July 2020 in six languages to members of WHO’s Global Clinical Practice Network composed of 15,500 mental health practitioners. The study assessed COVID-19’s impact on: work circumstances; occupational well-being; use and transition to telehealth; and expectations, needs and recommendations. 2,505 mental health professionals from 126 countries responded to the study (47% psychiatrists). 93.7% of respondents were currently practicing and 70.9% continued to see patients in person. The impact on clinical workload varied in terms of direction and extent depending on type of service provided and country of practice. Most participants had started or increased their use of telehealth services, and we identified a need for training to support telehealth use. Overall, clinicians scored high on well-being indices. However, a subset scored above the cutoff for low well-being and reported a significant number of post-traumatic symptoms. Five factors affected work-related stress: fear of infection, severe COVID-related events, life disruption, lack of adequate protection and role disruption. Data from this study will provide information relevant for the design, development, and integration of mental health services in the continuing pandemic, and in similar future scenarios. Disclosure No significant relationships.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".