The COVID-19 pandemic: an opportunity to make mental health a higher public health priority
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) was first recognised in December 2019. The subsequent pandemic has caused 4.3 million deaths and affected the lives of billions. It has increased psychosocial risk factors for mental illness including fear, social isolation and financial insecurity and is likely to lead to an economic recession. COVID-19 is associated with a high rate of neuropsychiatric sequelae. The long-term effects of the pandemic on mental health remain uncertain but could be marked, with some predicting an increased demand for psychiatric services for years to come. COVID-19 has turned a spotlight on mental health for politicians, policy makers and the public and provides an opportunity to make mental health a higher public health priority. We review longstanding reasons for prioritising mental health and the urgency brought by the COVID-19 pandemic, and highlight strategies to improve mental health and reduce the psychiatric fallout of the pandemic.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".