Psychometric evaluation of the Clinical Outcome in Routine Evaluation – General Population: Czech version
Bibliographic record
Abstract
Objectives. This study aimed to assess psychometric properties, such as reliability, construct validity, and cut-off scores, for the Czech version of the Clinical Outcome in Routine Evaluation – General Population (GP-CORE) questionnaire, a tool usable for repeated measurement of psychological distress within routine clinical settings. Participants and setting. Two general populations and one clinical sample were used with N values of 420, 394, and 345, respectively. Hypotheses. One of the competing theoretical factor solutions will demonstrate the best fit. Statistical analysis. To examine the factor structure of the GP-CORE, a confirmatory multidimensional item response theory analysis (graded response model) was employed. Results. The best fitting model was a bifactor solution representing one content domain of overall distress and two item wording domains (positively and negatively worded items). Clinical cut-off scores were determined to be 1.85 (men) and 1.90 (women). Study limitations. The GP-CORE can be used as an unidimensional measure of overall distress, but users have to be aware of the influence of positive vs. negative item wording on the responses.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".