Advancing Faculty Development in Medical Education
Bibliographic record
Abstract
PURPOSE: To (1) provide a detailed account of the nature and scope of faculty development (FD) programs in medical education, (2) assess the quality of FD studies, and (3) identify in what areas and through what means future research can purposefully build on existing knowledge. METHOD: The authors searched MEDLINE, CINAHL, and ERIC for articles reporting evaluations of FD initiatives published between 1989 and 2010. They applied standard systematic review procedures for sifting abstracts, scrutinizing full texts, and abstracting data, including program characteristics, evaluation methods, and outcomes. They used a modified Kirkpatrick model to guide their data abstraction. RESULTS: The authors included 22 articles reporting on 21 studies in their review. The most common program characteristics included a series/longitudinal format, intended for individuals, and offered to physicians only. Although the most common aim was to improve teaching effectiveness, several programs had multiple aims, including scholarship and leadership. Program evaluation focused on quantitative approaches. A number of studies employed longitudinal designs and included some follow-up component. Surveys were the most popular data collection method, participants the most common data source, and self-reported behavior changes the most commonly reported outcome. CONCLUSIONS: Although the authors' findings showed some recent expansion in the scope of the FD literature, they also highlighted areas that require further focus and growth. Future research should employ more rigorous evaluation methods, explore the role of interprofessional teams and communities of practice in the workplace, and address how different organizational and contextual factors shape the success of FD 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 | 0.052 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| 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".