Mental health interventions and supports during COVID- 19 and other medical pandemics: A rapid systematic review of the evidence
Bibliographic record
Abstract
BACKGROUND: Novel coronavirus pneumonia (COVID-19) is a global reminder of the need to attend to the mental health of patients and health professionals who are suddenly facing this public health crisis. In the last two decades, a number of medical pandemics have yielded insights on the mental health impact of these events. Based on these experiences and given the magnitude of the current pandemic, rates of mental health disorders are expected to increase. Mental health interventions are urgently needed to minimize the psychological sequelae and provide timely care to affected individuals. METHOD: We conducted a rapid systematic review of mental health interventions during a medical pandemic, using three electronic databases. Of the 2404 articles identified, 21 primary research studies are included in this review. RESULT: We categorized the findings from the research studies using the following questions: What kind of emotional reactions do medical pandemics trigger? Who is most at risk of experiencing mental health sequelae? What works to treat mental health sequelae (psychosocial interventions and implementation of existing or new training programs)? What do we need to consider when designing and implementing mental health interventions (cultural adaptations and mental health workforce)? What still needs to be known? CONCLUSION: Various mental health interventions have been developed for medical pandemics, and research on their effectiveness is growing. We offer recommendations for future research based on the evidence for providing mental health interventions and supports to those most in need.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".