Translation and validation of the Dutch version of the Effective Consumer Scale (EC-17)
Bibliographic record
Abstract
PURPOSE: The Effective Consumer Scale (EC-17) measures the skills of musculoskeletal patients in managing their own healthcare. The objectives of this study were to translate the EC-17 into Dutch and to further evaluate its psychometric properties. METHODS: The EC-17 was translated and cognitively pretested following cross-cultural adaptation guidelines. Two hundred and thirty-eight outpatients (52 % response rate) with osteoarthritis or fibromyalgia completed the EC-17 along with other validated measures. Three weeks later, 101 patients completed the EC-17 again. RESULTS: Confirmatory factor analysis supported the unidimensional structure of the scale. The items adequately fit the Rasch model and only one item demonstrated differential item functioning. Person reliability was high (0.92), but item difficulty levels tended to cluster around the middle of the scale, and measurement precision was highest for moderate and lower levels of skills. The scale demonstrated adequate test-retest reliability (ICC = 0.71), and correlations with other measures were largely as expected. CONCLUSION: The results supported the validity and reliability of the Dutch version of the EC-17, but suggest that the scale is best targeted at patients with relatively low levels of skills. Future studies should further examine its sensitivity to change in a clinical trial specifically aimed at improving effective consumer skills.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".