Innovations in digital interventions for psychological trauma: harnessing advances in cognitive science
Why this work is in the frame
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Bibliographic record
Abstract
A range of digital psychological interventions have demonstrated a positive impact on trauma-related problems in controlled trials, but there is room for further improvements in their form, reach and impact. Most to date have been adaptions of established face-to-face treatments. In this paper, we highlight a complementary emerging route to their development, which draws on advances in cognitive science theory and research and applies them to clinical contexts. Three examples are given regarding laboratory research with potential applications to digital interventions for trauma-related mental health problems: a digital game to reduce intrusive memories of trauma, novel cognitive techniques for worry, and digitally supported mental imagery to enhance motivation for functional behavior change. Much of this research is still at an early stage, meriting a balance of optimism and caution. However, even if only a few digital applications of cognitive science constitute substantial improvements to complement current treatments, their potential for large-scale use at low unit cost may provide significant benefits across populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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