Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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