The construct and criterion validity of the multi-source feedback process to assess physician performance: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to conduct a meta-analysis on the construct and criterion validity of multi-source feedback (MSF) to assess physicians and surgeons in practice. METHODS: In this study, we followed the guidelines for the reporting of observational studies included in a meta-analysis. In addition to PubMed and MEDLINE databases, the CINAHL, EMBASE, and PsycINFO databases were searched from January 1975 to November 2012. All articles listed in the references of the MSF studies were reviewed to ensure that all relevant publications were identified. All 35 articles were independently coded by two authors (AA, TD), and any discrepancies (eg, effect size calculations) were reviewed by the other authors (KA, AD, CV). RESULTS: Physician/surgeon performance measures from 35 studies were identified. A random-effects model of weighted mean effect size differences (d) resulted in: construct validity coefficients for the MSF system on physician/surgeon performance across different levels in practice ranged from d=0.14 (95% confidence interval [CI] 0.40-0.69) to d=1.78 (95% CI 1.20-2.30); construct validity coefficients for the MSF on physician/surgeon performance on two different occasions ranged from d=0.23 (95% CI 0.13-0.33) to d=0.90 (95% CI 0.74-1.10); concurrent validity coefficients for the MSF based on differences in assessor group ratings ranged from d=0.50 (95% CI 0.47-0.52) to d=0.57 (95% CI 0.55-0.60); and predictive validity coefficients for the MSF on physician/surgeon performance across different standardized measures ranged from d=1.28 (95% CI 1.16-1.41) to d=1.43 (95% CI 0.87-2.00). CONCLUSION: The construct and criterion validity of the MSF system is supported by small to large effect size differences based on the MSF process and physician/surgeon performance across different clinical and nonclinical domain measures.
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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 | 0.131 | 0.234 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.071 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".