Inter-rater reliability of the Dysexecutive Questionnaire (DEX): Comparative data from non-clinician respondents—all raters are not equal
Bibliographic record
Abstract
PRIMARY OBJECTIVE: The Dysexecutive Questionnaire (DEX) is used to obtain information about executive and emotional problems after neuropathology. The DEX is self-completed by the patient (DEX-S) and an independent rater such as a family member (DEX-I). This study examined the level of inter-rater agreement between either two or three non-clinician raters on the DEX-I in order to establish the reliability of DEX-I ratings. METHODS AND PROCEDURES: Family members and/or carers of 60 people with mixed neuropathology completed the DEX-I. For each patient, DEX-I ratings were obtained from either two or three raters who knew the person well prior to brain injury. MAIN OUTCOMES AND RESULTS: This study obtained two independent-ratings for 60 patients and three independent-ratings for 36 patients. Intra-class correlations revealed that there was only a modest level of agreement for items, sub-scale and total DEX scores between raters for their particular family member. Several individual DEX items had low reliability and ratings for the emotion sub-scale had the lowest level of agreement. CONCLUSIONS: Independent DEX ratings completed by two or more non-clinician raters show only moderate correlation. Suggestions are made for improving the reliability of DEX-I ratings.
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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 | 0.058 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 0.001 |
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".