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Record W2922277428 · doi:10.1097/acm.0000000000002695

Strengthening Teachers’ Professional Identities Through Faculty Development

2019· article· en· W2922277428 on OpenAlexaff
Yvonne Steinert, Patricia O’Sullivan, David M. Irby

Bibliographic record

VenueAcademic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsMentorshipFaculty developmentExcellenceIdentity (music)Professional developmentPublic relationsPedagogyMedical educationPsychologyPerspective (graphical)CollegialitySociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.054
GPT teacher head0.404
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations201
Published2019
Admission routes1
Has abstractyes

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