Mental health and psychosocial well-being during the COVID-19 pandemic: the invisible elephant in the room
Bibliographic record
Abstract
The novel SARS-CoV-2 coronavirus pandemic has emerged as a truly formidable threat to humankind's existence. In the wake of the massively volatile global situation created by COVID-19, it is vital to recognize that the trauma it causes can affect people in different ways, at the individual and collective levels, resulting in mental health challenges for many. Although mental health problems account for about one-third of the world's disability among adults, these issues tend to be under-addressed and overlooked in society and are closely associated with deadly disease outbreaks. In large scale outbreaks, the mental health problems experienced are not limited to infected persons but also extend to involve frontline health workers and community members alike. While it is crucial to limit the spread of infections during an outbreak, previous experience suggests that mental and behavioural health interventions should be fully included in public health response strategies.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".