The Reliability, Validity, and Feasibility of Multisource Feedback Physician Assessment
Bibliographic record
Abstract
PURPOSE: The use of multisource feedback (MSF) or 360-degree evaluation has become a recognized method of assessing physician performance in practice. The purpose of the present systematic review was to investigate the reliability, generalizability, validity, and feasibility of MSF for the assessment of physicians. METHOD: The authors searched the EMBASE, PsycINFO, MEDLINE, PubMed, and CINAHL databases for peer-reviewed, English-language articles published from 1975 to January, 2013. Studies were included if they met the follow ing inclusion criteria: used one or more MSF instruments to assess physician performance in practice; reported psychometric evidence of the instrument(s) in the form of reliability, generalizability coefficients, and construct or criterion-related validity; and provided information regarding the administration or feasibility of the process in collecting the feedback data. RESULTS: Of the 96 full-text articles assessed for eligibility, 43 articles were included. The use of MSF has been shown to be an effective method for providing feedback to physicians from a multitude of specialties about their clinical and nonclinical (i.e., professionalism, communication, interpersonal relationship, management) performance. In general, assessment of physician performance was based on the completion of the MSF instruments by 8 medical colleagues, 8 coworkers, and 25 patients to achieve adequate reliability and generalizability coefficients of α ≥ 0.90 and Ep ≥ 0.80, respectively. CONCLUSIONS: The use of MSF employing medical colleagues, coworkers, and patients as a method to assess physicians in practice has been shown to have high reliability, validity, and feasibility.
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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 | 0.183 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".