Bibliographic record
Abstract
The 2020 Vision of Faculty Development Across the Medical Education Continuum conference, and the resulting articles in this issue, addressed a number of topics related to the future of faculty development. Focusing primarily on the development of faculty members as teachers, conference participants debated issues related to core teaching competencies, barriers to effective teaching, competency-based assessment, relationship-centered care, the hidden curriculum that faculty members encounter, instructional technologies, continuing medical education, and research on faculty development. However, a number of subjects were not addressed. If faculty development is meant to play a leading role in ensuring that academic medicine remains responsive to faculty members and societal needs, additional themes should be considered. Medical educators should broaden the focus of faculty development and target the various roles that clinicians and basic scientists play, including those of leader and scholar. They must also remember that faculty development can play a critical role in curricular and organizational change and thus enlarge the scope of faculty development by moving beyond formal, structured activities, incorporating notions of self-directed learning, peer mentoring, and work-based learning. In addition, medical educators should try to situate faculty development in a more global context and collaborate with international colleagues in the transformation of medical education and health care delivery. It has been said that faculty development can play a critical role in promoting culture change at a number of levels. A broader mandate, innovative programming that takes advantage of communities of practice, and new partnerships can help to achieve this objective.
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 | 0.013 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.083 | 0.083 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".