Bibliographic record
Abstract
Abstract Faculty development refers to that broad range of activities institutions use to renew or assist faculty in their multiple roles as well as those activities individuals engage in independently to enhance their abilities as teachers and educators, researchers and scholars, and leaders and administrators. In response to multiple factors, faculty development has become an increasingly important component of medical education. The majority of faculty development programs focus on teaching improvement. However, faculty development can play a significant role in helping health‐care professionals become more effective researchers and leaders; it can also focus on academic career development and organizational change. Health‐care professionals develop their knowledge, skills, and abilities in a number of ways, including both formal and informal approaches. Formal faculty development initiatives include workshops and seminars, short courses, fellowships, and other longitudinal programs. Informal approaches include work‐based learning and belonging to a community of practice. Mentorship is also key in promoting professional development. Faculty development activities have been highly valued by health‐care professionals, many of whom report an impact on their knowledge, skills, and behaviors as a faculty member. Academic vitality is dependent upon faculty members' interest and expertise; faculty development plays a critical role in promoting academic excellence and vitality.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.008 |
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".