Cross-national examination of the Distress Tolerance Scale using Rasch methodology
Bibliographic record
Abstract
Abstract The 15-item Distress Tolerance Scale (DTS) is a widely used psychometric measure with evidence generally supporting its reliability and validity. However, limitations related to its ordinal response format and lack of cross-cultural comparisons have yet to be investigated using appropriate methods. The Partial Credit Rasch model was used to evaluate and enhance the psychometric properties of the DTS using responses from 2550 adult participants from the United States of America (USA), England, Canada, South Africa, Spain, and Argentina. The initial poor fit of the DTS to the Rasch model was improved by removing one item and combining locally dependent items into three testlets. These modifications resulted in the best fit of the 14-item DTS to the Rasch model for all the countries in our study, providing evidence of unidimensionality, high reliability and invariance across countries, meditation practice, and gender. Meeting the expectations of the Rasch model permitted the development of ordinal-to-interval conversion algorithms derived from person estimates of the Rasch model. Using the ordinal-to-interval conversion algorithms published in this article, ordinal DTS scores can be transformed into interval-level data, enhancing the precision of this scale for future research and clinical use across people from the six countries in this study and across the English and Spanish versions of the 14-item DTS.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.003 | 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".