Development of and Preliminary Validity Evidence for the EFeCT Feedback Scoring Tool
Bibliographic record
Abstract
BACKGROUND: Narrative feedback, like verbal feedback, is essential to learning. Regardless of form, all feedback should be of high quality. This is becoming even more important as programs incorporate narrative feedback into the constellation of evidence used for summative decision-making. Continuously improving the quality of narrative feedback requires tools for evaluating it, and time to score. A tool is needed that does not require clinical educator expertise so scoring can be delegated to others. OBJECTIVE: To develop an evidence-based tool to evaluate the quality of documented feedback that could be reliably used by clinical educators and non-experts. METHODS: Following a literature review to identify elements of high-quality feedback, an expert consensus panel developed the scoring tool. Messick's unified concept of construct validity guided the collection of validity evidence throughout development and piloting (2013-2020). RESULTS: The Evaluation of Feedback Captured Tool (EFeCT) contains 5 categories considered to be essential elements of high-quality feedback. Preliminary validity evidence supports content, substantive, and consequential validity facets. Generalizability evidence supports that EFeCT scores assigned to feedback samples show consistent interrater reliability scores between raters across 5 sessions, regardless of level of medical education or clinical expertise (Session 1: n=3, ICC=0.94; Session 2: n=6, ICC=0.90; Session 3: n=5, ICC=0.91; Session 4: n=6, ICC=0.89; Session 5: n=6, ICC=0.92). CONCLUSIONS: There is preliminary validity evidence for the EFeCT as a useful tool for scoring the quality of documented feedback captured on assessment forms. Generalizability evidence indicated comparable EFeCT scores by raters regardless of level of expertise.
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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 | 0.346 | 0.531 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".