Evaluating people's ability to assess treatment claims: Validating a test in Mandarin from Claim Evaluation Tools database
Bibliographic record
Abstract
OBJECTIVE: To describe the psychometric testing using Rasch analysis of a test in Mandarin developed from the Claim Evaluation Tools database. METHODS: We translated selected MCQs from the IHC Claim Evaluation Tools database to Mandarin and created a test including 24 MCQs covering 11 key concepts. We used purposeful sampling and surveyed children and adults in the Lanzhou area. In total 389 responses were entered into the analysis. We evaluated the psychometric properties of the test using Rasch analysis and the RUMM2030 software, testing for internal construct validity (multidimensionality), invariance of the items (item-person interaction), and item bias (differential item functioning). RESULTS: Overall, the psychometric properties of the test were found to be satisfactory. Based on findings from the Rasch analysis, we deleted three MCQs with suboptimal fit. CONCLUSIONS: The resulting test includes 21 MCQs and can be used in school and other teaching settings, in randomized trials evaluating outcomes of educational interventions, or in cross-sectional studies in Mandarin-speaking populations in China.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.030 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".