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The Role of Universal Health Literacy Precautions in Minimizing “Medspeak” and Promoting Shared Decision Making

2017· article· en· W2600301280 on OpenAlexaff
Lara Killian, Margo Coletti

Bibliographic record

VenueThe AMA Journal of Ethic · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsNova Scotia Health Authority
Fundersnot available
KeywordsJargonCornerstoneHealth literacyHealth careInterpreterLanguage barrierLiteracyNursingMedicinePsychologyMedical educationComputer sciencePolitical sciencePedagogyLinguistics

Abstract

fetched live from OpenAlex

Shared decision making (SDM), a collaborative process whereby patients and professionals make health care decisions together, is a cornerstone of ethical patient care.The patient-clinician communication necessary to achieve SDM depends on many factors, not the least of which is a shared language (sometimes with the aid of a medical interpreter).However, even when a patient and clinician are speaking the same mother tongue, the use of medical jargon can pose a large and unnecessary barrier.This article discusses how health care professionals can use "universal health literacy precautions" as a legal, practical, and ethical means to enhance SDM and improve health care outcomes. Case of Language Barrier's Impact on Patient AutonomyPatient X, a 56-year-old construction worker, visits the emergency department (ED) with pain and swelling in his right calf.He can walk on his leg with a limp and considers the pain to be bearable.He would prefer to just "power through" whatever is causing the discomfort.This is his first ED visit.He is here to placate his wife, who was alarmed at the progression of the swelling over the last 24 hours.A physical exam reveals a slightly elevated temperature of 99 degrees and blood pressure of 150/110.Blood cultures, a creatine phosphokinase (CPK) test, and a Creactive protein (CRP) test are ordered.When asked about any previous trauma to his leg, Patient X is confused.Has he had "trauma"?He considers himself a calm person, not easily alarmed or "traumatized."He asks the physician (Dr.Y) why they need his blood and is told that the cultures may reveal the agent or pathogen responsible for his condition.Patient X is becoming alarmed.To him, an "agent" sounds like a person.A "pathogen" sounds like a "psychopath" but it can't mean that, except the doctor said something about the "culture."Dr. Y is running behind; it will be at least two hours before she can return with the bloodwork results to confirm her diagnosis, prescribe appropriately, and either discharge or admit Patient X.This leaves Patient X with two hours to worry and try to explain the situation to his wife over the phone.

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)0.030
metaresearch head score (Gemma)0.088
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.088
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0040.008
Scholarly communication0.0050.005
Open science0.0010.009
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.165
GPT teacher head0.474
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.

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

Citations25
Published2017
Admission routes1
Has abstractyes

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