Translation, validation and exploration of the factor structure in the French version of the <i>Posttraumatic Cognitions Inventory</i> (PTCI)
Bibliographic record
Abstract
Le Posttraumatic Cognitions Inventory (PTCI) is one of the most used instruments to assess posttraumatic cognitions. Since its release, many studies have tried to validate and translate this questionnaire, but they had difficulty to confirm its structure and then suggested alternatives. Faced with no consensus, a short version in nine statements was developed and showed good psychometric properties. To date, no French version of the PTCI has been validated, thereby preventing studies from investigating the role of posttraumatic cognitions in French speaking populations. Objectives In order to validate a French version of the PTCI, this study investigates two objectives using two French speaking samples: (1) test 10 factor structures identified in prior studies, and (2) assess the other psychometric properties of the best fitting factor structure. Method The PTCI was translated in French using a reverse translation method and administered to 202 university students and 114 aid workers. Suitability indexes of the appropriate factor structures previously identified in prior studies were examined. Internal consistency, correlations between subscales and convergent, divergent and discriminant validities in the most appropriate structure were evaluated. Results Results support that only Wells et al.'s short 9-item version of the PTCI and three factors shows excellent suitability indexes. This version also outlines an excellent internal consistency and solid convergent, divergent, and discriminant validities. Conclusions This study confirms the empirical validity, fidelity, and utility of Wells et al.'s short version of the PTCI. This is the first PTCI French validation, which is a major advantage when it comes to assess posttraumatic cognitions in French trauma victims.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".