Do DSM-5 changes to PTSD symptom cluster criteria alter the frequency of probable PTSD when screening treatment-seeking Canadian Forces members and Veterans?
Bibliographic record
Abstract
Introduction: DSM-5 diagnostic criteria revisions for post-traumatic stress disorder (PTSD) have raised concerns about PTSD prevalence – particularly the new requirement of one avoidance symptom. We examined the frequency of positive screening results for probable PTSD in treatment-seeking Canadian Armed Forces (CAF) personnel and Veterans when both DSM-5 and DSM-IV-TR symptom cluster criteria were applied. Methods: Previously collected data from 382 CAF personnel and Veterans were used to identify the frequency of positive screens using both sets of diagnostic criteria. Results: 71.2% ( n=272) of participants screened positively for probable PTSD using DSM-5 symptom cluster criteria, compared to 77.7% ( n=297) using DSM-IV-TR symptom cluster criteria. Percent agreement analyses found that negative percent agreement was 100.0%, positive percent agreement was 91.6%, and overall percent agreement was 93.5%. Discussion: The number of individuals who screened positively for probable PTSD using DSM-IV-TR criteria was higher than those who screened positively using DSM-5 criteria. The requirement of at least one avoidance symptom appears to have a noticeable impact on the frequency of positive screens for probable PTSD among treatment-seeking military personnel. This has important implications for pension adjudication and treatment entitlement.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".