Asking for Less and Getting More: The Impact of Broadening a Rater’s Focus in Formative Assessment
Bibliographic record
Abstract
PURPOSE: There may be unintended consequences of broadening the competencies across which health professions trainees are assessed. This study was conducted to determine whether such broadening influences the formative guidance assessors provide to trainees and to test whether sequential collection of competency-specific assessment can overcome setbacks of simultaneous collection. METHOD: A randomized between-subjects experimental design, conducted in Toronto and Halifax, Canada, in 2016-2017 with paramedic educators experienced in observing/rating, in which observers' focus was manipulated. In the simultaneous condition, participants rated four unscripted (i.e., spontaneously generated) clinical performances using a six-dimension global rating scale and provided feedback. In three sequential conditions, participants were asked to rate the same performances and provide feedback but for only two of the six dimensions. Participants from these conditions were randomly merged to create a "full score" and set of feedback statements for each candidate. RESULTS: Eighty-seven raters completed the study; 23 in the simultaneous condition and 21 or 22 for each pair of dimensions in the sequential conditions. After randomly merging participants, there were 21 "full scores" in the sequential condition. Compared with the sequential condition, participants in the simultaneous condition demonstrated reductions in the amount of unique feedback provided, increased likelihood of ignoring some dimensions of performance, lessened variety of feedback, and reduced reliability. CONCLUSIONS: Sequential or distributed assessment strategies in which raters are asked to focus on less may provide more effective assessment by overcoming the unintended consequences of asking raters to spread their attention thinly over many dimensions of competence.
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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 | 0.110 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".