Epistemic Trust, Mistrust and Credulity Questionnaire (ETMCQ) validation in French language: Investigating association with loneliness
Bibliographic record
Abstract
Abstract The concept of epistemic trust is gaining traction in the mental health field. It is thought to play a foundational role as a resilience factor against the development and maintenance of psychopathology by facilitating social learnings useful to navigate in the modern world. The primary aim of this study is to validate in French language the Epistemic Trust, Mistrust, and Credulity Questionnaire (ETMCQ). We further investigate associations with key developmental and psychological factors (childhood trauma, mentalizing and attachment), besides possible mediating roles between childhood traumatic experiences and psychopathology and between loneliness and psychopathology. 302 participants were recruited for analysis via the online survey platform Prolific. Along with ETMCQ, measures of CTQ-SF, RFQ-8, ECR-R, UCLA-LS and SCL-90-R were administered. Confirmatory Factor Analysis and General Linear Model of Mediation were conducted. Our study shows that the ETMCQ represents a valid instrument to assess epistemic trust. We find an adequate replication of the original three-factor solution in a francophone population with a 12-item version, exhibiting satisfactory psychometric properties and external validity. We replicate previous findings that demonstrated epistemic trust’s attachment style related differences, as well as the mediating effect between childhood traumatic experiences and psychopathology. We also observed that epistemic trust mediates the well-described association between loneliness and psychopathology. We add momentum to the framework that considers epistemic trust as key underlying contributor to the maintenance or alleviation of psychopathology. Future research should investigate the ETMCQ in clinical population, where psychopathological expressions are severe, enduring and connected, and where identifying potential intercessors could help target and improve interventions.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".