Developing thinking around mental health science: the example of intrusive, emotional mental imagery after psychological trauma
Bibliographic record
Abstract
Introduction: One route to advancing psychological treatments is to harness mental health science, a multidisciplinary approach including individuals with lived experience and end users (e.g., Holmes, E. A., Craske, M. G., & Graybiel, A. M. (2014). Psychological treatments: A call for mental-health science. Nature, 511(7509), 287–289. doi:10.1038/511287a). While early days, we here illustrate a line of research explored by our group—intrusive imagery-based memories after trauma.Method/Results: We illustrate three possible approaches through which mental health science may stimulate thinking around psychological treatment innovation. First, focusing on single/specific target symptoms rather than full, multifaceted psychiatric diagnoses (e.g., intrusive trauma memories rather than all of posttraumatic stress disorder). Second, investigating mechanisms that can be modified in treatment (treatment mechanisms), rather than those which cannot (e.g., processes only linked to aetiology). Finally, exploring novel ways of delivering psychological treatment (peer-/self-administration), given the prevalence of mental health problems globally, and the corresponding need for effective interventions that can be delivered at scale and remotely for example at times of crisis (e.g., current COVID-19 pandemic).Conclusions: These three approaches suggest options for potential innovative avenues through which mental health science may be harnessed to recouple basic and applied research and transform treatment development.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".