What do I do? Developing a competency inventory for postgraduate (residency) program directors
Bibliographic record
Abstract
BACKGROUND: Few new Residency Program Directors (PD) are formally trained for the demands and responsibilities of the leadership aspect of their role. Currently, there are no comprehensive frameworks that describe specific leadership competencies that can inform PD self-reflection or faculty development. METHODS: The authors developed a Postgraduate Program Director Competency Inventory (PPDCI) in order to frame the performance of PDs for a multisource feedback (MSF) program. The development of the PPDCI occurred in five phases which involved: development of an initial inventory, implementation of a key informant survey of national opinion leaders, execution of a validity survey with postgraduate education leaders and committee members and implementation of a further refined inventory with 17 PD and 147 raters as part of a pilot MSF program. OUTCOMES: Five distinct domains of leadership competence were identified which included: Communication and relationship management, leadership, professionalism and self-management, environmental engagement, and management skills and knowledge. The content validity of the PPDCI was endorsed by 85% of the key informants. The validity survey indicated strong endorsement of the PPDCI domains and recognition of its utility for both orientation of new PD as well as a frame for self-assessment. The pilot MSF program yielded a further refined and reduced inventory of 26 items of competence as well as recommendations for its utility. CONCLUSIONS: Use of this leadership inventory has the potential to ensure effective leadership of postgraduate programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".