Using big data to advance mental health research
Bibliographic record
Abstract
Over the past decade there has been an increasing awareness of the potential for data science to make important advances in brain and mental health research. This focus has coincided with the use of electronic health records in the clinic, the advent of electronic remote data capture through smart devices, publicly available de-identified large data sets, and the emergence of research consortia collaborating on the analysis of complex data sets. Interest in data science in mental health research has been further heightened by the widely acknowledged need for improved diagnostic precision and individualised risk prediction, long-term monitoring, and treatment. Using big data approaches to tackle complex mental health problems is now felt to be a key research priority moving forward. Therefore, Evidence Based Mental Health has …
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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 | 0.046 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.022 | 0.046 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".