The Illness-Related Distress Scale: development and psychometric evaluation of a new transdiagnostic measure
Bibliographic record
Abstract
Abstract Background Individuals with long-term physical health conditions (LTCs) experience higher rates of depression and anxiety. Conventional self-report measures do not distinguish distress related to LTCs from primary mental health disorders. This difference is important as treatment protocols differ. We developed a transdiagnostic self-report measure of illness-related distress, applicable across LTCs. Methods The new Illness-Related Distress (IRD) scale was developed through thematic coding of interviews, systematic literature search, think-aloud interviews with patients and healthcare providers, and expert-consensus meetings. An internet sample (n = 1,398) of UK-based individuals with LTCs completed the IRD scale for psychometric analysis. We randomly split the sample (1:1) to conduct: (1) an exploratory factor analysis (EFA; n = 698) for item reduction, and (2) iterative confirmatory factor analysis (CFA; n = 700) and exploratory structural equation modeling (ESEM). Here, further item reduction took place to generate a final version. Measurement invariance, internal consistency, convergent, test–retest reliability, and clinical cut-points were assessed. Results EFA suggested a 2-factor structure for the IRD scale, subsequently confirmed by iteratively comparing unidimensional, lower order, and bifactor CFAs and ESEMs. A lower-order correlated 2-factor CFA model (two 7-item subscales: intrapersonal distress and interpersonal distress) was favored and was structurally invariant for gender. Subscales demonstrated excellent internal consistency, very good test–retest reliability, and good convergent validity. Clinical cut points were identified (intrapersonal = 15, interpersonal = 12). Conclusion The IRD scale is the first measure that captures transdiagnostic distress. It may aid assessment within clinical practice and research related to psychological adjustment and distress in LTCs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".