Becoming a deliberately developmental organization: Using competency based assessment data for organizational development
Bibliographic record
Abstract
Medical education is situated within health care and educational organizations that frequently lag in their use of data to learn, develop, and improve performance. How might we leverage competency-based medical education (CBME) assessment data at the individual, program, and system levels, with the goal of redefining CBME from an initiative that supports the development of physicians to one that also fosters the development of the faculty, administrators, and programs within our organizations? In this paper we review the Deliberately Developmental Organization (DDO) framework proposed by Robert Kegan and Lisa Lahey, a theoretical framework that explains how organizations can foster the development of their people. We then describe the DDO's conceptual alignment with CBME and outline how CBME assessment data could be used to spur the transformation of health care and educational organizations into digitally integrated DDOs. A DDO-oriented use of CBME assessment data will require intentional investment into both the digitalization of assessment data and the development of the people within our organizations. By reframing CBME in this light, we hope that educational and health care leaders will see their investments in CBME as an opportunity to spur the evolution of a developmental culture.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".