Test–retest reliability of the Cost for Patients Questionnaire
Bibliographic record
Abstract
OBJECTIVES: To investigate the test-retest reliability of the Costs for Patients Questionnaire (CoPaQ). METHODS: Through an online survey, individuals were invited to participate in a two-step study to assess the test-retest reliability of the CoPaQ. Participants to the first step were invited to complete the questionnaire a second time 2 weeks after. Reliability was assessed by calculating Cohen's Kappa coefficients and intraclass correlation coefficients (ICC) for discrete and continuous data, respectively. A sensitivity analysis was carried out. RESULTS: From a total of 1,200 participants who completed the first test, 403 completed the second test. The ICC varied from -0.00 to 0.98 with poor, moderate, good, and excellent results. The Kappa coefficients varied from -0.004 to 0.65 and were poor, slight, fair, moderate, and substantial. The sensitivity analysis showed the median value of ICC and Kappa coefficients for each category before and after the outliers' exclusion. The median value of ICC changed from 0.30 (before) to 0.70 (after), and from 0.12 (before) to 0.04 (after), respectively, for each category. The median value of the Cohen's Kappa coefficient increased from 0.44 (before) to 0.46 (after) and decreased from 0.32 (before) to 0.30 (after), respectively. CONCLUSIONS: Test-retest reliability results indicated that the CoPaQ has a moderate reliability in terms of ICC and Kappa coefficients. The moderate reliability observed gives additional support for the applicability of this tool in economic evaluations of health interventions. Additional studies including on other properties and a cultural adaptation could further enhance the use of the tool.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".