PTSD Coach around the world
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) is a global public health problem. Unfortunately, many individuals with PTSD do not receive professional care due to a lack of available providers, stigma about mental illness, and other concerns. Technology-based interventions, including mobile phone applications (apps) may be a viable means of surmounting such barriers and reaching and helping those in need. Given this potential, in 2011 the U.S Veterans Affairs National Center for PTSD released PTSD Coach, a mobile app intended to provide psycho-education and self-management tools for trauma survivors with PTSD symptoms. Emerging research on PTSD Coach demonstrates high user satisfaction, feasibility, and improvement in PTSD symptoms and other psychosocial outcomes. A model of openly sharing the app's source code and content has resulted in versions being created by individuals in six other countries: Australia, Canada, The Netherlands, Germany, Sweden, and Denmark. These versions are described, highlighting their significant adaptations, enhancements, and expansions to the original PTSD Coach app as well as emerging research on them. It is clear that the sharing of app source code and content has benefited this emerging PTSD Coach community, as well as the populations they are targeting. Despite this success, challenges remain especially reaching trauma survivors in areas where few or no other mental health resources exist.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.064 |
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".