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Record W156292065

Room for a view : A transformation

2001· article· en· W156292065 on OpenAlexvenueno aff
Kerri Lambert

Bibliographic record

VenueCanadian Medical Association Journal · 2001
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsMistakeConversationMedicineValue (mathematics)PsychologyPediatricsPsychoanalysisLawCommunication
DOInot available

Abstract

fetched live from OpenAlex

I am not like most doctors. The marvels of medicine do not fascinate me. Rare syndromes and abnormal findings do not inspire me. Physiology and pharmacology do not leave me breathless. I do not like medicine. As I force myself out of bed each morning and put on my happy face for work, I am convinced that I should quit. Yet somehow I make it through the day. I return home physically and mentally exhausted. My extracurricular activities have become a chore. I let them slide, which is a mistake, since I clearly need an outlet. Nonmedical friends tell me to give it up and go back to teaching. My medical friends cannot sympathize, since their lives are just as hellish, or worse: some are in surgery. Acquaintances and relatives can't believe I would consider leaving this noble profession after I've “come so far.” I'm in a pediatrics residency. I like everything about it except the medicine. I love what most people dislike. I love developmental, behavioural and psychosocial pediatrics. I love to play, to read and to cut and paste with children. Unfortunately, a busy service, many admissions, presentations, journal clubs and mock oral exams leave little time for play in or outside the hospital. I remain in pediatrics because I see things somewhat differently. I value emotional medicine more than the science of disease. A recent conversation with one of our patients troubles me. He was being worked up for subacute bacterial endocarditis. This adorable four-year-old looked healthy but continued to spike temperatures. Our team entered his room to listen to his murmur. He welcomed us with a smile and laughter. When the third person (of five) was listening to his heart, he was told we needed more blood. He was going to get poked. He began to whimper. You're going to take blood? Why? Are you going to take it now? Will it hurt? Can my Mommy stay with me? What will it feel like? Are you going to do it now? Mommy, I don't want another needle! Can you do it tomorrow? What kind of band-aid will I get? His questions were limitless. We answered each one honestly. The medical student listening to his chest asked him nicely to please be quiet, so she could hear his heart. He complied for about 45 seconds and then began firing more questions. The next person to examine him prefaced her auscultation with a reminder to be quiet. I wish I had interrupted the clinical exam and tried to alleviate his fears. Instead, I only helped to stifle them, for both he and I stayed quiet. Guilt also plagues the next memory. Surgery was my first rotation as a medical student. I had just finished the first history and physical of my medical career. My patient had hepatomegaly. An abdominal ultrasound and CT showed hepatic cancer. The prognosis was poor: only six months to live. During morning rounds, our team of five walked into his room at 6:20. He was alone. His wife and family were at home. After asking him how he was feeling, the attending physician gave him the news. In front of five strangers, our patient found out he had cancer. “So you're sure it's cancer?” he asked. “I'm afraid so.” The team proceeded to the other side of the curtain to ask the next patient if he'd passed any gas. I next see my patient staring out a hallway window at 3 a.m. My first inclination was to leave him alone with his thoughts. But I turned back to talk to him. Selfishly, I needed to talk to him. I feel guilty at the memory of it: when we talked, he was trying to make me feel better. I feel guilty about the manner in which he was told of his impending death. I feel guilty knowing about his impending death. I still search obituaries for his name, although I know he has passed away. Medicine brings joy into millions of lives. Unfortunately, accompanying this joy is overwhelming hardship. This misery explains my attraction to psychosocial medicine. I interpret its sorrow with less morbidity. On our service is a teenager with pseudoseizures. Neurology has cleared her of any pathology. Her tox screen is negative. She is a top student who has a boyfriend, plays hockey, works two jobs, is bulimic and desperately wants to talk to her mother but does not know how. Her teenage angst has finally manifested itself in pseudoseizures. I find this fascinating from both a medical and a personal viewpoint. Rejecting my woes and shutting out the world is, at times, pathetically attractive. Sadly, catatonia has become enchanting. These thoughts make me want to quit and to remain in medicine at the same time. A wonderful senior resident once told me to look for something unique in each patient, to look for my own “take-home message.” For me, the breathtaking, heartstopping moments in medicine occur when I gain my young patients' trust and they let me play with them. In a profession that I permit to rob me of my energy and emotional strength, I hope to find stability. I hope to strike an equilibrium between my life and my profession, hoping that in this way I will find happiness. But if, in my search for serenity, I choose a path away from medicine, I will still treasure what I have gained: the ability to transform patients back into people.

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)0.015
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0300.038
Scholarly communication0.0300.032
Open science0.0040.033
Research integrity0.0090.031
Insufficient payload (model declined to judge)0.0430.020

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 head0.030
GPT teacher head0.367
Teacher spread0.337 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2001
Admission routes1
Has abstractyes

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