Bibliographic record
Abstract
Abstract The Global Burden of Disease studies have consistently shown that mental health challenges are among the leading causes of lost disability-adjusted life years (DALYs), and represent one of the biggest public health problems of our time. The World Health Organization (WHO) estimates that around 450 million people struggle with mental health issues at any one time - this is only exacerbated by the current pandemic. Now, more than ever, there is strong argument for WHO proposition of ‘no health without mental health'. The current weapon for tackling the mental health crisis is largely based on a medical model. After decades of medication-based treatments, this model has seen a significant shift towards ‘talking therapies'. More so, the last few years, particularly during the pandemic, virtual care has become the significant mechanism of providing mental health support. Our research has identified a number of challenges in the current approach that remain unaddressed: 1) recovery rates from talking therapies are low and inequitably distributed; 2) improvements in mental health depend more on individual-level factors and circumstances than the person providing therapy; and 3) most people who recover from mental health problems relapse within a year. In this session, we will discuss these findings and ask: are we approaching mental health with the right approach, or is this time to rewrite the vows between public health and primary care to tackle the mental health crisis?
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.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.040 | 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".