Validity evidence for Quality Improvement Knowledge Application Tool Revised (QIKAT-R) scores: consequences of rater number and type using neurology cases
Bibliographic record
Abstract
OBJECTIVES: To develop neurology scenarios for use with the Quality Improvement Knowledge Application Tool Revised (QIKAT-R), gather and evaluate validity evidence, and project the impact of scenario number, rater number and rater type on score reliability. METHODS: Six neurological case scenarios were developed. Residents were randomly assigned three scenarios before and after a quality improvement (QI) course in 2015 and 2016. For each scenario, residents crafted an aim statement, selected a measure and proposed a change to address a quality gap. Responses were scored by six faculty raters (two with and four without QI expertise) using the QIKAT-R. Validity evidence from content, response process, internal structure, relations to other variables and consequences was collected. A generalisability (G) study examined sources of score variability, and decision analyses estimated projected reliability for different numbers of raters and scenarios and raters with and without QI expertise. RESULTS: Raters scored 163 responses from 28 residents. The mean QIKAT-R score was 5.69 (SD 1.06). G-coefficient and Phi-coefficient were 0.65 and 0.60, respectively. Interrater reliability was fair for raters without QI expertise (intraclass correlation = 0.53, 95% CI 0.30 to 0.72) and acceptable for raters with QI expertise (intraclass correlation = 0.66, 95% CI 0.02 to 0.88). Postcourse scores were significantly higher than precourse scores (6.05, SD 1.48 vs 5.22, SD 1.5; p < 0.001). Sufficient reliability for formative assessment (G-coefficient > 0.60) could be achieved by three raters scoring six scenarios or two raters scoring eight scenarios, regardless of rater QI expertise. CONCLUSIONS: Validity evidence was sufficient to support the use of the QIKAT-R with multiple scenarios and raters to assess resident QI knowledge application for formative or low-stakes summative purposes. The results provide practical information for educators to guide implementation decisions.
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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 | 0.502 | 0.821 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".