Defining new roles and competencies for administrative staff and faculty in the age of competency-based medical education
Bibliographic record
Abstract
These authors sought to define the new roles and competencies required of administrative staff and faculty in the age of CBME. A modified Delphi process was used to define the new CBME roles and competencies needed by faculty and administrative staff. We invited international experts in CBME (volunteers from the ICBME Collaborative email list), as well as faculty members and trainees identified via social media to help us determine the new competencies required of faculty and administrative staff in the CBME era. Thirteen new roles were identified. The faculty-specific roles were: National Leader/Facilitator in CBME; Institutional/University lead for CBME; Assessment Process & Systems Designer; Local CBME Leads; CBME-specific Faculty Developers or Trainers; Competence Committee Chair; Competence Committee Faculty Member; Faculty Academic Coach/Advisor or Support Person; Frontline Assessor; Frontline Coach. The staff-specific roles were: Information Technology Lead; CBME Analytics/Data Support; Competence Committee Administrative Assistant. The authors present a new set of faculty and staff roles that are relevant to the CBME context. While some of these new roles may be incorporated into existing roles, it may be prudent to examine how best to ensure that all of them are supported within all CBME contexts in some manner.
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 | 0.044 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".