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Record W4405402889 · doi:10.1080/0142159x.2024.2429612

Disentangling faculty development: A scoping review towards a rich description of the concept and its practice

2024· review· en· W4405402889 on OpenAlexaff
Susan van Schalkwyk, Eliana Amaral, Megan Anakin, Ruth Chen, Diana Dolmans, Ardi Findyartini, Noeline Fobian, Karen Leslie, Jana Müller, Patricia O’Sullivan, Subha Ramani, Olanrewaju Sorinola, Farhan Vakani, Daya Yang, Yvonne Steinert

Bibliographic record

VenueMedical Teacher · 2024
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversity of TorontoSickKids FoundationHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsMedical educationEngineering ethicsHealth professionsFaculty developmentPsychologyMedicineProfessional developmentHealth carePolitical scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: There is wide variation in how faculty development (FD) is practiced globally and described in the literature. This scoping review aims to clarify how FD is conceptualised and practiced in health professions education. METHODOLOGY: Using a systematic search strategy, 418 papers, published between 2015-2023, were included for full text review. We extracted data using closed and open-ended questions. Quantitative data were summarised using descriptive statistics and qualitative data synthesised using content analysis. RESULTS: was the most frequently used term encompassing a range of understandings and practices. Many papers focused on educators' enhanced understanding of teaching, learning, and assessment. Several highlighted the social context of collaborative practice and organisational learning. FD formats included workshops, courses, longitudinal programs, and coaching and mentoring. Dominant conceptual frameworks included Kirkpatrick's model of evaluation, communities of practice theory, adult learning theory, and experiential learning. CONCLUSIONS: Although FD continues to evolve in response to the dynamic HPE landscape, this growth needs to be accelerated. To facilitate meaningful collaboration across professions, contexts, and countries, attention must be paid to terms used and meanings ascribed to them. Those responsible for FD need to think anew about its purpose and practice, demonstrating flexibility as the ever-changing context demands.

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)0.002
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.794
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.157
GPT teacher head0.483
Teacher spread0.325 · 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 designOther design
Domainnot available
GenreReview

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

Citations12
Published2024
Admission routes1
Has abstractyes

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