Making judgments based on reported observations of trainee performance: a scoping review in Health Professions Education
Bibliographic record
Abstract
Background: Educators now use reported observations when assessing trainees' performance. Unfortunately, they have little information about how to design and implement assessments based on reported observations. Objective: The purpose of this scoping review was to map the literature on the use of reported observations in judging health professions education (HPE) trainees' performances. Methods: Arksey and O'Malley's (2005) method was used with four databases (sources: ERIC, CINAHL, MEDLINE, PsycINFO). Eligibility criteria for articles were: documents in English or French, including primary data, and initial or professional training; (2) training in an HPE program; (3) workplace-based assessment; and (4) assessment based on reported observations. The inclusion/exclusion, and data extraction steps were performed (agreement rate > 90%). We developed a data extraction grid to chart the data. Descriptive analyses were used to summarize quantitative data, and the authors conducted thematic analysis for qualitative data. Results: Based on 36 papers and 13 consultations, the team identified six steps characterizing trainee performance assessment based on reported observations in HPE: (1) making first contact, (2) observing and documenting the trainee performance, (3) collecting and completing assessment data, (4) aggregating assessment data, (5) inferring the level of competence, and (6) documenting and communicating the decision to the stakeholders. Discussion: The design and implementation of assessment based on reported observations is a first step towards a quality implementation by guiding educators and administrators responsible for graduating competent professionals. Future research might focus on understanding the context beyond assessor cognition to ensure the quality of meta-assessors' decisions.
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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 | 0.171 | 0.439 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.039 | 0.034 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".