Measuring incompleteness and not just right experiences: A psychometric evaluation of two commonly used questionnaires in OCD and anxiety disorders samples
Bibliographic record
Abstract
Extending previous research, this study examined the psychometric properties of two commonly used self-report measures of incompleteness (INC) and not-just-right experiences (NJREs), the Obsessive-Compulsive Trait Core Dimensions Questionnaire (OC-TCDQ; Summerfeldt et al., 2014) and the Not Just Right Experiences Questionnaire - Revised (NJRE-QR; Coles et al., 2003) in large samples of individuals with OCD and anxiety disorders. Factor analyses indicated adequate support for a two-factor solution for the OC-TCDQ and a one-factor solution for the NJRE-QR. Both measures demonstrated excellent internal consistency and good-to-excellent test-retest reliability. We found good convergent validity between the measures of interest and with an OCD symptom severity measure. Discriminant validity was evidenced by a significantly stronger correlation between INC and NJRE severity than the relatively modest correlations with theoretically distinct constructs (i.e., harm avoidance and general distress). Individuals with OCD had a similar number of NJREs as individuals with anxiety disorders but reported significantly greater NJRE distress and levels of INC. Finally, both measures were sensitive to change across group cognitive-behavioural therapy for OCD. These findings provide support for the reliability and validity of the OC-TCDQ and NJRE-QR to measure INC (trait) and NJRE (state) constructs that assist in understanding the phenomenology of OCD. • Assessed OC-TCDQ & NJRE-QR psychometrics in large OCD and anxiety disorders samples. • OC-TCDQ & NJRE-QR demonstrated good to excellent reliability and validity. • OC-TCDQ & NJRE-QR severity scores decreased with CBT, showing sensitivity to change. • OC-TCDQ & NJRE-QR are suitable measures of INC (trait) and NJRE (state) respectively.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".