Faculty and Resident Perspectives on Using Entrustment Anchors for Workplace-Based Assessment
Bibliographic record
Abstract
BACKGROUND: Research suggests that workplace-based assessment (WBA) tools using entrustment anchors provide more reliable assessments than those using traditional anchors. There is a lack of evidence describing how and why entrustment anchors work. OBJECTIVE: The purpose of this study is to better understand the experience of residents and faculty with respect to traditional and entrustment anchors. METHODS: We used constructivist grounded theory to guide data collection and analysis (March-December 2017) and semistructured interviews to gather reflections on anchors. Phase 1 involved residents and faculty (n = 12) who had only used assessment tools with traditional anchors. Phase 2 involved participants who had used tools with entrustment anchors (n = 10). Data were analyzed iteratively. RESULTS: , enabling better feedback. Participants with no prior experience using entrustment anchors outlined contextual concerns regarding their use. Participants with experience described how they addressed these concerns. Participants expressed that entrustment anchors leave a gap in assessment information because they do not provide normative data. CONCLUSIONS: Insights from this analysis contribute to a theoretical framework of benefits and challenges related to the adoption of entrustment anchors. This richer understanding of faculty and resident perspectives of entrustment anchors may assist WBA developers in creating more acceptable tools and inform the necessary faculty development initiatives that must accompany the use of these new WBA tools. .
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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 | 0.032 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".