Bibliographic record
Abstract
Multisource feedback (MSF), or 360-degree employee evaluation, is a questionnaire-based assessment method in which rates are evaluated by peers, patients, and coworkers on key performance behaviors. Although widely used in industrial settings to assess performance, the method is gaining acceptance as a quality improvement method in health systems. This article describes MSF, identifies the key aspects of MSF program design, summarizes some of the salient empirical research in medicine, and discusses possible limitations for MSF as an assessment tool in health care. In industry and in health care, experience suggests that MSF is most likely to succeed and result in changes in performance when attention is paid to structural and psychometric aspects of program design and implementation. A carefully selected steering committee ensures that the behaviors examined are appropriate, the communication package is clear, and the threats posed to individuals are minimized. The instruments that are developed must be tested to ensure that they are reliable, achieve a generalizability coefficient of Ep2 = .70, have face and content validity, and examine variance in performance ratings to understand whether ratings are attributable to how the physician performs and not to factors beyond the physician's control (e.g., gender, age, or setting). Research shows that reliable data can be generated with a reasonable number of respondents, and physicians will use the feedback to contemplate and initiate changes in practice. Performance may be affected by familiarity between rater and ratee and sociodemographic and continuing medical education characteristics; however, little of the variance in performance is explained by factors outside the physician's control. MSF is not a replacement for audit when clinical outcomes need to be assessed. However, when interpersonal, communication, professionalism, or teamwork behaviors need to be assessed and guidance given, it is one of the better tools that may be adopted and implemented to provide feedback and guide performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.099 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".