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
Mental health plays a central role in people’s lives and is intrinsically tied to many other aspects of people’s wider well-being. This was underscored during the COVID-19 pandemic, when direct health impacts and loss of lives combined with social isolation, loss of work and financial insecurity all contributed to a significant worsening of people’s mental health. Data from 15 OECD countries suggest that by late 2020, over one-quarter of people experienced symptoms of depression or anxiety. Already, well before the pandemic hit, it was estimated that half of the population will experience a mental health condition at least once in their lifetime and the economic costs of mental ill-health amounted to more than 4% of GDP annually. Good mental health, on the other hand, can boost people’s resilience to stress, help them realise their goals and actively contribute to their communities. Positive mental health, or having high levels of emotional and psychological well-being, is also increasingly being recognised as policy target in its own right by health and other government agencies across the OECD.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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