Strengthening Teachers’ Professional Identities Through Faculty Development
Bibliographic record
Abstract
Although medical schools espouse a commitment to the educational mission, faculty members often struggle to develop and maintain their identities as teachers. Teacher identity is important because it can exert a powerful influence on career choice, academic roles and responsibilities, and professional development opportunities. However, most faculty development initiatives focus on knowledge and skill acquisition rather than the awakening or strengthening of professional identity. The goal of this Perspective is to highlight the importance of faculty members' professional identities as teachers, explore how faculty development programs and activities can support teachers' identities, and describe specific strategies that can be used in professional development. These strategies include the embedding of identity and identity formation into existing offerings by asking questions related to identity, incorporating identity in longitudinal programs, building opportunities for community building and networking, promoting reflection, and capitalizing on mentorship. Stand-alone faculty development activities focusing on teachers' identities can also be helpful, as can a variety of approaches that advocate for organizational change and institutional support. To achieve excellence in teaching and learning, faculty members need to embrace their identities as teachers and be supported in doing so by their institutions and by faculty development.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".