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Record W2790091064 · doi:10.1097/acm.0b013e31824485ac

Teaching and Learning Moments

2012· article· en· W2790091064 on OpenAlexaff
Robin Hopkins

Bibliographic record

VenueAcademic Medicine · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Philosophies and Pedagogies
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsPsychoanalysisSorrowGriefPsychologyFeelingSadnessLaughterFriendshipSisterGratitudeAestheticsSocial psychologyArtAngerSociologyPsychotherapist

Abstract

fetched live from OpenAlex

A Negotiation Between Death and Learning As I look at the cadaver on the gurney in front of me, I am overwhelmed. Not because I am about to cut up the flesh of a human being but because this body was someone's person. The person who knew everything about that someone—her past, her fears, and the little things that annoyed her or made her smile. The person she told everything to, shared her day with, and confided in. The person she was connected to through friendship, memories, and love. That this was someone's person makes me pause before lifting my scalpel. In German, there are two words for the body—korper, the physical body, and leib, the living body. My immediate perception of the cadaver is leib, the living body. As I lay my hand over her cold skin, a wave of sadness rushes through me as I grieve with those who have lost their person, perhaps a mother, a sister, or a friend. Feelings of the recent loss of my person are rekindled. Tears well in my eyes not so much that they spill down my cheeks but enough that my vision is blurred, focusing my attention inward on my experience of the emotions of death. I feel the spirit of her living body, her presence resonating at the forefront of my perception. As my tears subside, my grief transforms into gratitude and thanks. My emotions shift from the icy gray of sorrow to a warm lavender of appreciation for the gift of learning I have just received. My attention again turns to her cold skin, this time mindful of my task at hand—to respectfully use this gift to its fullest potential. I cradle the scalpel in the palm of my hand and press the tip of the blade down to penetrate the flesh. Like an archaeologist labeling strata of sediment, I work downward differentiating between the thin covering of skin, yellow lobules of fat, white sheets of connective tissue, linear rows of muscle fibers, and, finally, the abdominal cavity. As I peel back the outermost covering that personified this body, my perception shifts from leib to korper. My emotions fade into the background as I become entrenched in exploring her internal structure. I stand amazed at the intricate arrangement of her physical, anatomical body. The distinct colorful organs in my anatomy textbook are like a rainbow compared to the monochrome gray of the intermingled viscera before me. I clear away the connective tissue, working methodically, impersonally, detached. I use my probe to trace the path of an artery, its diverse pattern of branching subtly reminding me of the body as leib, a living body with unique characteristics, returning me to a place of reflection. This cadaver, someone's person, has given me the gift of learning through the donation of her body. My emotions resurge to the forefront, the warmth of gratitude countering the cool air of the lab. I make a promise to her, and her person, to respectfully learn all that I can from what she's given me. I carry on the dance between exploring, defining, and identifying structures in her physical body and being reminded of the loss of this living person. I negotiate the outward, manual tasks of cutting, probing, and identifying with the inward reflections of thanks for the gift of her body. Dissection is learning—learning anatomy and learning to face death. The experience is a flux between the explicit task of dissecting, gaining knowledge of the physical body, and implicit emotional reflection, acknowledging the life of a person and the gift of her body to help others learn.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.119
GPT teacher head0.445
Teacher spread0.326 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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