Translating science into policy: mental health challenges during the COVID-19 pandemic
Bibliographic record
Abstract
Several stressors associated with the coronavirus disease 2019 (COVID-19) are expected to affect the mental health of global populations: the effects of physical distancing, quarantine, and social isolation; the emotional suffering of health and other frontline workers; neuropsychiatric sequelae in those affected by the virus; the impact to families of lives lost to the disease; differential effects for those with severe mental disorders; and the consequences of social and economic deterioration. In this context, we sought: to form a panel of Brazilian experts on child and adolescent health, neurodevelopment, health services, and adult and elderly mental health; and to compile evidence-based interventions to support suggested policy changes in Brazil to mitigate the expected increase in mental health disorders during the pandemic and its mental health consequences. The following actions are recommended: 1) invest in prevention programs for the safe return of students to schools; 2) adopt evidence-based psychosocial interventions to maintain an adequate environment for child and adolescent development; 3) target socially vulnerable populations and those experiencing discrimination; 4) train primary care teams to solve common mental health problems, provide needs-based assessments, and manage long-term, at-home care for older patients; 5) invest in technological advancements (e.g., telemedicine, e-Health, and web-based algorithms) to promote coordinated care; 6) increase access to and literacy in the use of computers and mobile phones, especially among older adults; 7) expand protocols for remote, brief psychotherapy interventions and psychoeducation to manage common mental health problems.
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".