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Record W2322759634 · doi:10.1097/acm.0b013e3181ef5e51

Letʼs Be Clear

2010· article· en· W2322759634 on OpenAlexaff
Cathy Risdon

Bibliographic record

VenueAcademic Medicine · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyHostilityMedical educationOrientation (vector space)MedicineSocial psychology

Abstract

fetched live from OpenAlex

In addition to the usual information, such as procedures, logins, EMR operations, security passcards, and evaluation paperwork, covered on the first day of residency, my colleagues and I at the family medicine teaching clinic also want our residents to learn about our culture. And so the new chief resident, my codirector, and I gathered together a group of 20 or so orientation-weary R1s on their first day. I began, “This time of year is always a bit poignant. We've just said goodbye to our outgoing residents who have spent the past two years with us. We're excited to be starting new relationships with you. Over the years, we've found that about 20% of our graduating residents are lovely human beings, fantastic physicians, and great colleagues. You'll meet some of these individuals because we ask them to return as locums and, occasionally, as junior faculty. The majority of our grads, about 75% or so, are also great people, and we enjoy working with them. They make very good family physicians, and we keep in touch.” I paused for a second, noticing that several people were doing the math in their heads. “Frankly, because of the frustration and hostility they cause, we can't wait for the remaining five percent to leave. This orientation is to tell you what you need to know so that you don't find yourself in that five percent.” There was a long pause. Many of the residents stared at us in open shock; several mouths hung open. Then there was a round of nervous laughter. For the next 30 minutes, we shared how we hoped our residents would behave, as well as a few instances in which our residents strayed into “The Five Percent” in the past. Despite our shocking revelation, “The Five Percent” of residents is, in reality, a much smaller percentage of the group. And although we encounter residents who need remediation for academic and personal problems, they aren't the ones who incite any degree of animus. The ones who stick out in our minds are the residents who seem difficult—they complain, they create work for others, and they see patients as the enemy. In my experience, it's very difficult to address this pattern of behavior early enough so the resident may learn from his or her mistake without the incident being too emotional. Because of the deep frustration, even anger, these patterns of behavior can bring about in preceptors and peers, residents exhibiting these behaviors are often simply avoided. Or we focus instead exclusively on their cognitive evaluations because addressing how their attitudes and coping strategies affect others makes us uncomfortable. Finally, since we so often assume that others know what we expect of them (isn't it obvious?), our frustration is doubled when we see learners behave in ways that seem so deliberately annoying or concerning. This year's R1s have begun their rotation with a heightened awareness that their behavior really does impact other people and that first impressions on the team are important. Several have met with us, saying “I don't want to be in that five percent.” Best of all, if we do see something troubling, we have an easy way to introduce our concerns and start an early and corrective conversation. But I suspect we won't need to because they still remember what we said at orientation and won't easily forget their shock at our honesty. Cathy Risdon, MD, DMan

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)0.006
metaresearch head score (Gemma)0.045
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: Editorial · Consensus signal: none
Teacher disagreement score0.288
Threshold uncertainty score0.964

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0130.016
Open science0.0030.009
Research integrity0.0090.018
Insufficient payload (model declined to judge)0.2880.209

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 head0.110
GPT teacher head0.512
Teacher spread0.402 · 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
GenreEditorial

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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Citations4
Published2010
Admission routes1
Has abstractyes

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