Development and testing of an assessment instrument for the formative peer review of significant event analyses
Bibliographic record
Abstract
AIM: To establish the content validity and specific aspects of reliability for an assessment instrument designed to provide formative feedback to general practitioners (GPs) on the quality of their written analysis of a significant event. METHODS: Content validity was quantified by application of a content validity index. Reliability testing involved a nested design, with 5 cells, each containing 4 assessors, rating 20 unique significant event analysis (SEA) reports (10 each from experienced GPs and GPs in training) using the assessment instrument. The variance attributable to each identified variable in the study was established by analysis of variance. Generalisability theory was then used to investigate the instrument's ability to discriminate among SEA reports. RESULTS: Content validity was demonstrated with at least 8 of 10 experts endorsing all 10 items of the assessment instrument. The overall G coefficient for the instrument was moderate to good (G>0.70), indicating that the instrument can provide consistent information on the standard achieved by the SEA report. There was moderate inter-rater reliability (G>0.60) when four raters were used to judge the quality of the SEA. CONCLUSIONS: This study provides the first steps towards validating an instrument that can provide educational feedback to GPs on their analysis of significant events. The key area identified to improve instrument reliability is variation among peer assessors in their assessment of SEA reports. Further validity and reliability testing should be carried out to provide GPs, their appraisers and contractual bodies with a validated feedback instrument on this aspect of the general practice quality agenda.
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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 | 0.308 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".