“School of hard knocks” – what can mental health researchers learn from the COVID‐19 crisis?
Bibliographic record
Abstract
Since the COVID-19 pandemic took hold in the first quarter of 2020, children and their families across the world have experienced extraordinary changes to the way they live their lives - creating enormous practical and psychological challenges for them at many levels. While some of these effects are directly linked to COVID-related morbidity and mortality, many are indirect - due rather to governmental public health responses designed to slow the spread of infection and minimise the numbers of deaths. These have often involved aggressive programmes of social distancing and quarantine, including extended periods of national social and economic lockdown, unprecedented in the modern age. Debates about the appropriateness of these measures have often referenced their potentially negative impact on people's mental health and well-being - impacts which both opponents and advocates appear to accept as being inevitable.
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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 | 0.071 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.060 |
| Scholarly communication | 0.029 | 0.060 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.031 | 0.067 |
| Insufficient payload (model declined to judge) | 0.013 | 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".