Estimating classification consistency of screening measures and quantifying the impact of measurement bias.
Bibliographic record
Abstract
Screening measures are used in psychology and medicine to identify respondents who are high or low on a construct. Based on the screening, the evaluator assigns respondents to classes corresponding to different courses of action: Make a diagnosis versus reject a diagnosis; provide services versus withhold services; or conduct further assessment versus conclude the assessment process. When measures are used to classify individuals, it is important that the decisions be consistent and equitable across groups. Ideally, if respondents completed the screening measure repeatedly in quick succession, they would be consistently assigned into the same class each time. In addition, the consistency of the classification should be unrelated to the respondents' background characteristics, such as sex, race, or ethnicity (i.e., the measure is free of measurement bias). Reporting estimates of classification consistency is a common practice in educational testing, but there has been limited application of these estimates to screening in psychology and medicine. In this article, we present two procedures based on item response theory that are used (a) to estimate the classification consistency of a screening measure and (b) to evaluate how classification consistency is impacted by measurement bias across respondent groups. We provide R functions to conduct the procedures, illustrate the procedures with real data, and use Monte Carlo simulations to guide their appropriate use. Finally, we discuss how estimates of classification consistency can help assessment specialists make more informed decisions on the use of a screening measure with protected groups (e.g., groups defined by gender, race, or ethnicity). (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.430 | 0.770 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".