Psychometric Validation of the English and French Versions of the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5)
Bibliographic record
Abstract
The purpose of this study is to assess the psychometric properties of a French version of the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), a self-report measure of posttraumatic stress disorder (PTSD) symptoms, and to further validate the existing English version of the measure. Undergraduate students (n = 838 English, n = 262 French) completed the PCL-5 as well as other self-report symptom measures of PTSD and depression online. Both the English and French versions PCL-5 total scores demonstrated excellent internal consistency (English: α = .95; French: α = .94), and strong convergent and divergent validity. Strong internal consistency was also observed for each of the four subscales for each version (α's > .79). Test-retest reliability for the French version of the measure was also very good (r = .89). Confirmatory factor analysis indicated that the four-factor DSM-5 model was not a good fit of the data. The seven-factor hybrid model best fit the data in each sample, but was only marginally superior to the six-factor anhedonia model. The French version of the PCL-5 demonstrated the same psychometric qualities as both the English version of the same measure and previous versions of the PCL. Thus clinicians serving French-speaking clients now have access to this highly used screening instrument. With regards to the structural validity of the PCL-5 and of the new PTSD diagnostic structure of the DSM-5, additional research is warranted. Replication of our results in clinical samples is much needed.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".