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Record W2591363945 · doi:10.1097/acm.0000000000001545

Learning Professionalism Under Stress

2017· article· en· W2591363945 on OpenAlexaff
Benjamin Chin‐Yee

Bibliographic record

VenueAcademic Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCompassionHonestyEmpathyPsychologyAngerOffensivePatienceBurnoutBlameMedicineMedical educationSocial psychologyLawClinical psychology

Abstract

fetched live from OpenAlex

As a medical trainee, developing professionalism is a core goal of my education. Unlike technical and knowledge-based competencies, professionalism captures an all-encompassing attitude; it is more of a virtue than a skill. In an ideal world, the virtues of professionalism—honesty, integrity, commitment, compassion, respect, altruism—would govern all human interactions. However, in health care, professional attitudes are often challenged in emotional, stressful, and tiring situations. One particular patient encounter during my emergency medicine rotation highlighted this challenge. The young man arrived by ambulance at 5:00 am near the end of my overnight shift. He had been in an altercation and had suffered knife injuries to his face. He was intoxicated and belligerent; his swearing reverberated throughout the department, announcing his presence to us. As he was led into the ER, I noted multiple lacerations across his cheeks and forehead from which he was bleeding profusely. The nurses’ attempts to clean away the blood were met by offensive sexual comments. I approached the patient and our gazes met. “Quit looking at me like you want to fuck me!” he swore, jumping towards me. My initial shock gave way to anger, and I felt my blood boil with indignation. The staff physician intervened, conveying to the patient that his behavior was inappropriate and that he needed to cooperate to allow us to help him. The physician’s attempts to reason with the patient were only met with further curses and racial slurs. Ultimately, the patient was restrained and sedated so that we could attend to his injuries. When we reentered the room, we found him lying unconscious, intermittently groaning under heavy sedation. We proceeded to suture his wounds. In contrast to his previous aggressive demeanor, he now appeared pathetic and helpless. Any anger from our previous encounter had dissipated. Earlier, I had struggled to foster empathy while witnessing his abuses, but now, as he lay in restraints with torn clothing soaked in blood and dirt, I was suddenly overcome with feelings of guilt. I tried to imagine the circumstances that might have contributed to his current state. Despite our similar ages, I thought about how different our lives had been, about the privileges that I had enjoyed that he may have lacked. I felt guilty for my initial reaction, for having been angry, for judging him. This episode made me reflect on the challenge of remaining professional, especially in extreme situations where intense emotions and stress can cause us to forget the virtues of ethical practice and to revert to baser reactions. Being a physician certainly demands a high standard of ethical behavior. Nevertheless, this standard of professionalism does not necessarily entail a stoic notion of perfect equanimity. We all have human reactions and trying to eliminate these altogether, I believe, would harm our clinical practice. Just as anger and aversion can negatively impact patient care, joy, hope, and sadness can be harnessed to make for more meaningful doctor–patient relationships. This experience taught me that being a professional does not necessarily mean erasing all the negative emotions that one might feel. Instead, it involves developing the capacity to reflect on and to counteract initial reactions, recognizing how such feelings can adversely impact patient care. One strategy that helped me in this case was a deliberate and self-conscious attempt to foster empathy. The patient and I were, after all, not so different in age, perhaps separated only by luck and circumstance. As I continue in medicine, I hope that this experience will leave me better equipped to deal with these situations in a way that is not only professional but also human. Acknowledgments: The author wishes to thank Dr. Chris Willer, Dr. Sheena Taylor, and the student members of his Portfolio group for fruitful discussions on professionalism in medicine. He also wishes to thank the anonymous Faculty Scholar who provided feedback on this reflection and encouraged him to submit it for publication. Benjamin Chin-Yee, MA

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)0.005
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0080.010
Scholarly communication0.0100.005
Open science0.0010.012
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0130.005

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 head0.332
GPT teacher head0.621
Teacher spread0.289 · 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
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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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