Assessing the utility of a novel entrustment‐supervision assessment tool
Bibliographic record
Abstract
BACKGROUND: Work-based assessments (WBAs) are increasingly used to inform decisions about trainee progression. Unfortunately, WBAs often fail to discriminate between trainees of differing abilities and have poor reliability. Entrustment-supervision scales may improve WBA performance, but there is a paucity of literature directly comparing them to traditional WBA tools. METHODS: The Ottawa Emergency Department Shift Observation Tool (O-EDShOT) is a previously published WBA tool employing an entrustment-supervision scale with strong validity evidence. This pre-/post-implementation study compares the performance of the O-EDShOT with that of a traditional WBA tool using norm-based anchors. All assessments completed in 12-month periods before and after implementing the O-EDShOT were collected, and generalisability analysis was conducted with year of training, trainees within year and forms within trainee as nested factors. Secondary analysis included assessor as a factor. RESULTS: A total of 3908 and 3679 assessments were completed by 99 and 116 assessors, for 152 and 138 trainees in the pre- and post-implementation phases respectively. The O-EDShOT generated a wider range of awarded scores than the traditional WBA, and mean scores increased more with increasing level of training (0.32 vs. 0.14 points per year, p = 0.01). A significantly greater proportion of overall score variability was attributable to trainees using the O-EDShOT (59%) compared with the traditional tool (21%, p < 0.001). Assessors contributed less to overall score variability for the O-EDShOT than for the traditional WBA (16% vs. 37%). Moreover, the O-EDShOT required fewer completed assessments than the traditional tool (27 vs. 51) for a reliability of 0.8. CONCLUSION: The O-EDShOT outperformed a traditional norm-referenced WBA in discriminating between trainees and required fewer assessments to generate a reliable estimate of trainee performance. More broadly, this study adds to the body of literature suggesting that entrustment-supervision scales generate more useful and reliable assessments in a variety of clinical settings.
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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".