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Record W1994373723 · doi:10.1007/s40037-012-0021-4

A faculty development workshop in narrative-based reflective writing

2012· article· en· W1994373723 on OpenAlexafffund
J. Donald Boudreau, Stephen Liben, Abraham Fuks

Bibliographic record

VenuePerspectives on Medical Education · 2012
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMontreal Children's HospitalMcGill University
FundersMax Bell FoundationMcGill UniversityArnold P. Gold Foundation
KeywordsNarrativeDeliverableSet (abstract data type)Computer scienceFaculty developmentProfessional developmentMedical educationReflection (computer programming)Mathematics educationReflective writingPedagogyPsychologyMedicineEngineering

Abstract

fetched live from OpenAlex

Narrative approaches are used increasingly in the health professions with a range of objectives. We must acquaint educators with this burgeoning field and prepare them for the incorporation of story-telling in their pedagogical practices. The authors describe a template for a faculty development workshop designed to foster self-reflection through the use of narrative techniques and prepare clinical teachers to deploy such approaches. The design is based on a six-year experience in delivering introductory workshops in narrative approaches to medical teachers. The workshops, which served as a model for the template, have been offered to a total of 92 clinicians being trained to mentor medical students. A generic template is described. It includes a table of core concepts from narrative theory, a set of probing questions useful in a basic technical analysis of texts and a list of initiating prompts for exercises in reflective writing. A workshop organized and deployed using this template is deliverable over a half-day. The model has proven to be feasible and highly valued by participants. It can be adapted for other contexts by educators across the continuum of health professional education.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.416
Teacher spread0.370 · 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 designQualitative
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

Citations15
Published2012
Admission routes2
Has abstractyes

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