Evaluation of three abbreviated versions of the PTSD Checklist in Canadian Armed Forces personnel
Bibliographic record
Abstract
Introduction: Post-deployment screening within the Canadian Armed Forces (CAF) aims to identify individuals with mental health problems. However, as screening is a time-consuming process, it is important to consider ways to reduce the time required, including the use of shorter scales. The scale currently used to assess posttraumatic stress disorder (PTSD), the PTSD Checklist (PCL-C), is lengthy, although validated shorter versions have been developed that have not yet been evaluated in the CAF population. Methods: Three brief versions of the PCL-C were evaluated in this study: the PCL-2, PCL-4 and PCL-6. The operating characteristics of each scale were examined using the screening and diagnostic cut-offs of the full PCL, as well as clinician ratings of PTSD being of major concern, as the standards for comparison. Optimal cut-offs for each scale were determined based on a combination of sensitivity, specificity, area under the curve (AUC), and prevalence of disorder compared to the full scale. As well, correlations with other measures of health were examined. Results: Although all three scales demonstrated good psychometric properties, the PCL-6 showed the strongest properties of the three scales. Optimal cut-offs were similar to those found in past research when calibrated against the PCL-C screening cut-off for PTSD and to clinician ratings. As well, it exhibited high correlations with other measures of mental health. Discussion: This research provides evidence for the acceptability of brief measures in screening for PTSD in military members following deployment. In particular, it points to the advantages of using the PCL-6, with cut-offs in line with those recommended in past research.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".