Contextualizing Work-Based Assessments of Faculty and Residents: Is There a Relationship Between the Clinical Practice Environment and Assessments of Learners and Teachers?
Bibliographic record
Abstract
PURPOSE: Competence is bound to context, yet seldom is environment explicitly considered in work-based assessments. This study explored faculty and residents' perspectives of the environment during internal medicine clinical teaching unit (CTU) rotations, the extent that each group accounts for environmental factors in assessments, and relationships between environmental factors and assessments. METHOD: From July 2014 to June 2015, 212 residents and 54 faculty across 5 teaching hospitals at University of Toronto rated their CTU environment using a novel Practice Environment Rating Scale (PERS) matched by block and hospital. Faculty-PERS data were paired to In-Training Evaluation Reports (ITERs) of residents supervised during each block, and Resident-PERS data to Resident Assessment of Teaching Effectiveness (RATE) scores of the same faculty. Differences between perceptions and assessments were tested using repeated-measures MANOVAs, ANOVAs, and correlations. RESULTS: One-hundred sixty-four residents completed the PERS; residents rated the CTU environment more positively than faculty (3.91/5 vs. 3.29, P < .001). Residents were less likely to report considering environmental factors when assessing faculty (2.70/5) compared with faculty assessing residents (3.40, P < .0001), d = 1.2. Whereas Faculty-PERS ratings did not correlate with ITER scores, Resident-PERS ratings had weak to moderate correlations with RATE scores (overall r = 0.27, P = .001). CONCLUSIONS: Residents' perceptions of the environment had small but significant correlations with assessments of faculty. Faculty's perceptions did not affect assessments of residents, potentially because they reported accounting for environmental factors. Understanding the interplay between environment and assessment is essential to developing valid competency judgments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".