Opening up mental health research
Bibliographic record
Abstract
Open science provides a compelling framework for accelerating global collaborations and enabling discoveries to understand and treat mental health disorders. Herein, we discuss the advantages and obstacles to adopting open science in mental health research, considering the particularities of sensitive and diverse data types, the potential of co-designing projects with research participants and the opportunity of amplifying open science by integration with mental health care. We present a practical example of how this landscape may be navigated to adopt open science across an entire research centre, in 5 steps, namely leadership committing to open science; finding models, resources and allies; identifying needs; defining open science principles; and putting principles into practice. We derive lessons learned that can be built upon by researchers and research organizations joining the open science movement in mental health.
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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 | 0.209 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.022 | 0.076 |
| Scholarly communication | 0.036 | 0.050 |
| Open science | 0.005 | 0.066 |
| Research integrity | 0.017 | 0.035 |
| Insufficient payload (model declined to judge) | 0.017 | 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 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".