Evaluation of and Feedback for Academic Medicine Leaders: Developing and Implementing the Memorial Method
Bibliographic record
Abstract
PROBLEM: Giving and receiving honest and helpful feedback for leadership development is a common challenge in all types of organizations but particularly in academic medicine. APPROACH: At Memorial University of Newfoundland, in 2014, a consensus emerged to develop a new method for evaluating the leadership performance of the discipline chairs, dean, and vice dean, and to provide these leaders with the evaluation results to help them improve their performance. The leaders responsible for developing and implementing this method (called the Memorial Method) decided to use a survey to obtain faculty members' perceptions about their leader's performance. Beginning in October 2014, a portion of several regular meetings of the discipline chairs with the dean and vice dean was used to develop the survey, by first discussing the broad dimensions of leadership performance, then discussing these dimensions in more detail and drafting specific questions. The resulting survey included 44 quantitative questions addressing eight leadership dimensions. In March-April 2015, the survey was administered electronically to full-time faculty members on a confidential basis. The results were compiled and reported to each discipline chair and to the dean and vice dean. OUTCOMES: In total, 144/249 faculty responded to the survey (response rate: 58%). For the various dimensions, individual chairs' mean scores ranged from 2.82 to 4.70, and overall mean scores ranged from 3.57 to 4.24. Psychometric properties of the survey suggested it was both reliable and valid. NEXT STEPS: The survey will be repeated, this time with part-time as well as full-time faculty included.
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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 | 0.156 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".