An examination of psychometric properties of study quality assessment scales in meta-analysis: Rasch measurement model applied to the firefighter cancer literature
Bibliographic record
Abstract
Most existing quality scales have been developed with minimal attention to accepted standards of psychometric properties. Even for those that have been used widely in medical research, limited evidence exists supporting their psychometric properties. The focus of our current study is to address this gap by evaluating the psychometrics properties of two existing quality scales that are frequently used in cancer observational research: (1) Item Bank on Risk of Bias and Precision of Observational Studies developed by the Research Triangle Institute (RTI) International and (2) Newcastle-Ottawa Quality Assessment Scale (NOQAS). We used the Rasch measurement model to evaluate the psychometric properties of two quality scales based on the ratings of 49 studies that examine firefighters' cancer incidence and mortality. Our study found that RTI and NOQAS have an acceptable item reliability. Two raters were consistent in their assessment, demonstrating high interrater reliability. We also found that NOQAS has more items that show better fit than the RTI scale. The NOQAS produced lower study quality scores with a smaller variation, suggesting that NOQAS items are much easier to rate. Our findings accord with a previous study, which conclude that the RTI scale was harder to apply and thus produces more heterogenous quality scores than NOQAS. Although both RTI and NOQAS showed high item reliability, NOQAS items are better fit to the underlying construct, showing higher validity of internal structure and stronger psychometric properties. The current study adds to our understanding of the psychometric properties of NOQAS and RTI scales for future meta-analyses of observational studies, particularly in the firefighter cancer literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".