Exploring Content Relationships Among Components of a Multisource Feedback Program
Bibliographic record
Abstract
INTRODUCTION: A new multisource feedback (MSF) program was specifically designed to support physician quality improvement (QI) around the CanMEDS roles of Collaborator , Communicator , and Professional . Quantitative ratings and qualitative comments are collected from a sample of physician colleagues, co-workers (C), and patients (PT). These data are supplemented with self-ratings and given back to physicians in individualized reports. Each physician reviews the report with a trained feedback facilitator and creates one-to-three action plans for QI. This study explores how the content of the four aforementioned multisource feedback program components supports the elicitation and translation of feedback into a QI plan for change. METHODS: Data included survey items, rater comments, a portion of facilitator reports, and action plans components for 159 physicians. Word frequency queries were used to identify common words and explore relationships among data sources. RESULTS: Overlap between high frequency words in surveys and rater comments was substantial. The language used to describe goals in physician action plans was highly related to respondent comments, but less so to survey items. High frequency words in facilitator reports related heavily to action plan content. DISCUSSION: All components of the program relate to one another indicating that each plays a part in the process. Patterns of overlap suggest unique functions conducted by program components. This demonstration of coherence across components of this program is one piece of evidence that supports the program's validity.
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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 | 0.029 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".