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How a Professional Identity is Forged in the Anatomy Lab for Medical Students

2015· article· en· W2253776749 on OpenAlexaff
Claudia Krebs, Anita Parhar, Gurdeep Parhar

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedical educationCurriculumPerceptionFocus groupIdentity (music)PsychologyProfessional developmentAnatomyMedicinePedagogySociology

Abstract

fetched live from OpenAlex

It is recognized that the development of a professional identity is significantly influenced by experiences in academic and clinical settings, and through certain rites of passage, such as, learning from cadavers. Little is known, however, about how the experiences in the cadaver lab actively shape the development of the Professional. To understand the impact the experiences in the anatomy lab have on students' professional identity formation, first year students were surveyed before and their first anatomy session. Four months after commencing their anatomy sessions, students completed another survey and focus groups were conducted. In both the survey and focus groups, students were asked to explore their perceptions of themselves as students, physicians in training, idealized physicians, and the congruency between values held and behaviors displayed. When asked to rank their own development and the perception of their peers in the anatomy lab, students positioned themselves on the pathway towards the medical professional. They ranked those students coping best with the experience as closest to the qualities of a physician they admire. The emphasis on professionalism in the course had a positive impact on their experience. While cadavers have historically been used to teach anatomy, the findings from this study indicate that the same anatomy labs significantly contribute to the formation of the professional. As medical curricula across the world continue to evolve, the goal should be to preserve the rich experience gained by cadaver dissection.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.168
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2015
Admission routes1
Has abstractyes

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