Assessing psychologists in practice: Lessons from the health professions using multisource feedback.
Bibliographic record
Abstract
The aim of the present study was to conduct a systematic literature review of multisource feedback (MSF) instruments and to summarize the evidence of feasibility, reliability, generalizability, validity, and other psychometric characteristics of the instruments. Accordingly, we conducted a systematic literature review for English-language studies published from 1975 to 2012 using the following databases: MEDLINE, EMBASE, CINAHL, PubMed, and PsycINFO. The following terms were used in the search: multisource feedback, 360-degree evaluation, and assessment of medical professionalism. Forty-eight studies conducted in Canada, the United States, the United Kingdom, Netherlands, China, and elsewhere met the inclusion criteria. The results indicate that MSF has adequate evidence of validity, reliability, and feasibility for providing health practitioners with quality improvement data (both formative and summative assessment) as part of an overall strategy of maintaining competence and certification. Professional psychology has not adopted MSF as a systematic competence-based method for evaluating, maintaining, and assuring competent practice of psychology and instead relies on self-assessment as the primary quality assurance approach for its public accountability. We make recommendations to adopt an MSF system of competence-based assessment of practicing psychologists by regulatory and licensing authorities in Canada and the United States.
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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 | 0.187 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.005 |
| 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".