Bibliographic record
Abstract
Instructors in the health professions today must acquire knowledge and competencies that go beyond disciplinary expertise. It is now generally accepted that educational training as a teacher is essential to a faculty member's effectiveness as an educator. The educational challenges across the health professions share many similarities. In this article, we draw on the medical education literature and focus on faculty development designed to enhance teaching effectiveness. We first address commonly included faculty development topics, including instructional improvement, organizational development, the development of professional academic skills, and the teaching of specific content areas. We then review a variety of educational approaches and formats that are described in the literature. Included in this discussion are commonly used workshops, seminars, short courses, and fellowships, as well as longitudinal programs, peer coaching, mentorship, self-directed learning, and computer-aided instruction. We also briefly explore learning at work and in communities of practice, and we discuss several frequently encountered challenges in designing and implementing faculty development activities, including motivating colleagues and assessing program effectiveness. We conclude the discussion by presenting a set of guidelines for the design of effective faculty development programs.
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 | 0.055 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".