The COVID-19 pandemic: an opportunity to make mental health a higher public health priority
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it