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After the Match

2015· article· en· W2317263484 on OpenAlexaboutno aff
Thomas Cook

Bibliographic record

VenueEmergency Medicine News · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMalpracticeQuarter (Canadian coin)PsychologyMedical educationMedicineLawPolitical scienceHistory

Abstract

fetched live from OpenAlex

FigureFigureResidency is a 1,000-day process that prepares you for the next 10,000 days of your life. Real-life issues, from legal to financial to interpersonal, take on an entirely different light once you enter independent practice. But residents can — and should — develop three skills to prepare for these future challenges. First, learn about malpractice. Roughly two-thirds of you will go through this, and male physicians are twice as likely to be sued as women. Truth be told, however, the risk of ending up in court with someone pointing a finger at you is very small. The vast majority of cases never go to trial. About one-quarter settle, and of the five percent that go to trial, 90 percent end in favorable decisions for the physicians. Still, malpractice ends up being feared like shark attacks. They are not common, but they have a lot of teeth. An effective way to learn about the medicolegal system is to get involved in mock trials that are often conducted by teaching hospitals using attorneys from a local malpractice carrier. You will see how trials are conducted and how attorneys behave in the courtroom. We put on mock trials regularly at our program, and this is one conference in which you do not see anyone fall asleep. Also find out which of your faculty members are expert witnesses in malpractice litigation. What are the key issues in cases they have reviewed? Learning from others' mistakes allows you to focus on what not to miss in your patient care. Residents must also acquire basic information about personal finance. You will read lots of articles that provide a list of things to do to secure your financial future. But you are a resident; you do not have the time. But doing just two things will make a difference. First, find a financial advisor. Most financial advisors will fall over themselves to get you as a client. Not because you have any money, but because you will make a lot in the future. Good financial advisors will guide you through all the anxiety. They will calm you down when you panic about your crazy student loan debt. They will hold your hand and tell you about the other successful physician clients they have that went through the exact same process you are going through now. Finding a good one is easy; just ask your attendings. They all want to retire someday, and they have been investing money for years to do so. Secondly, learn how to use Quicken or some other personal finance software. Doing this will teach you the most important lesson about your future finances and how money flows into and out of your life. You will really be able to see how you spend your money. Try to complete both of these tasks before you are halfway through residency. It will make life easier when potential employers start throwing big numbers at you to join their practice. The last skill to acquire is the most important. Learn how to conduct a “crucial conversation.” Interpersonal relationships create the most happiness but also the most chaos in your life. It is ironic that the most intense arguments you will ever have will be with the people you profess to care about the most. This includes your spouse, your children, your peers, and eventually your partners. You need to be able to identify which conversations are tricky and how to create a safe environment in which to negotiate. Learning how to have a civil conversation with your spouse about money or where to spend the holidays is just as important as a tough conversation with a demanding patient and his family. If you are interested in learning more about how to do this, I suggest the book Crucial Conversations: Tools for Talking When Stakes are High by Kerry Patterson, et al. It is a quick read, and you will immediately learn strategies for solving problems that you encounter every day. Access the linksin EMN by reading this on our website or in our free iPad app, both available atwww.EM-News.com. Comments?Write to us at[email protected].

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.8910.739

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 head0.193
GPT teacher head0.503
Teacher spread0.310 · 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.

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
Published2015
Admission routes1
Has abstractyes

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