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Record W2332424212 · doi:10.1097/acm.0b013e3182613f48

Physicianship Amongst Physicians-in-Training

2012· article· en· W2332424212 on OpenAlexaff
Jacques Balayla

Bibliographic record

VenueAcademic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsConversationHumanismMedical educationPsychologyMedicinePhilosophyCommunication

Abstract

fetched live from OpenAlex

As medical students, we are taught to consider the patient, not simply the diagnosis. This concept, known as physicianship, encourages us to validate and dignify the lives of our patients while empathically looking for pertinent clues and findings to diagnose their condition. Despite its inherent humanism, we often hesitate to practice physicianship because, in our clinical experiences, carrying on a conversation with a patient does not extend far beyond the science of his or her condition. During medical school, an encounter with a patient, Mr. O, led me to realize that the distinction between science and physicianship is not always clear and that, in medicine, the two often overlap. On an otherwise typical Tuesday afternoon in the clinic, I was assigned to see the last patient of the day, Mr. O. As usual, I entered the exam room and said hello. Mr. O was a 78-year-old gentleman who appeared quite healthy upon my initial exam. I spent the next 30 minutes completing my duties as a medical student—taking a history, checking his vital signs. During this time, I did most of the talking. I learned that, since his last visit, Mr. O had achieved his weight loss goal and had regained full range of motion in his previously injured knee. Despite the good news, I recognized that Mr. O still was dissatisfied. I doubted that his dissatisfaction was with me or with our recent interaction. Instead, I sensed that he was looking for someone simply to listen to him. Was it possible that not one of his doctors had taken the time recently to listen to what he had to say if what he had to say wasn’t the answer to a question on his chart? Because I had extra time before my supervisor would be ready to review Mr. O’s case with me, I decided to spend that time with Mr. O in the hopes of remedying his apparent loneliness in addition to his physical ailments. I started with the question “What do you like to do for fun?” Out of nothing, something remarkable evolved. For the next half hour, Mr. O did all of the talking. Uncontrollably, he spoke, yelled, cried, and laughed, ridding himself of some of the pain that he had carried for so many years. Slowly, he slid his hand into his pocket and retrieved the list of his prescriptions. He stared intently at the name tag that hung from my white coat and began to write my name on the same piece of paper. He hugged me and politely asked, “Can I add you to my list of medications?” If I ever doubted the merits of physicianship, Mr. O reinforced them in a way that I will never forget. That day, I thanked God not for my voice or for my name but for having blessed me with the ability to listen. I learned how simple caring for patients could be—just be there to listen to them. As medical students, we may not be licensed to write prescriptions yet, but our white coats are, more often than not, the safest, most powerful medication that our patients need. Author’s Note: The name in this essay has been changed to protect the identity of the patient.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.092
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0100.004
Scholarly communication0.0080.006
Open science0.0010.014
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0920.017

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 head0.069
GPT teacher head0.382
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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