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Record W2320885619 · doi:10.1097/acm.0000000000000322

Reality Check

2014· article· en· W2320885619 on OpenAlexaffabout
Matthew J. To

Bibliographic record

VenueAcademic Medicine · 2014
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRefugeeFamily medicineMedicineHealth careSession (web analytics)PediatricsHemoglobin electrophoresisPsychologyAnemiaPolitical sciencePsychiatryLawComputer science

Abstract

fetched live from OpenAlex

Groggy medical students and a hematologist gathered around the table on a Monday morning for a tutorial session. We were there to discuss the case of Iman, a seven-year-old child who came to the clinic for a routine examination. He and his family had recently immigrated to Canada as refugees. As my colleagues and I began the discussion, we scrutinized lab results, which showed microcytic anemia. Our group discussed the differential diagnosis, then we collectively decided to order iron studies and a hemoglobin electrophoresis. The tests uncovered what we suspected, and a diagnosis of beta thalassemia minor was given to Iman. Ours were brief exchanges about different kinds of thalassemias, treatment options, and side effects. We asked questions and answered them. It seemed like a relatively simple exercise. As our time was winding down, we considered what impact our diagnosis would have on Iman and his family. I paused for a moment and scanned the patient description. Iman’s refugee status grabbed my attention. “I don’t think Iman would even have been diagnosed,” I blurted out. Some of my colleagues gave me puzzled glances. I explained: “I don’t think he would have received care under current Canadian regulations because many refugees are not eligible for routine medical examinations. The government recently made significant cuts to refugee health care.” Some of my colleagues understood what I was referring to and some looked surprised. The recent cuts prevented some refugees from receiving basic health services, like prenatal screening and routine medical examinations. Without health insurance coverage, many refugees were discouraged from seeking proper health care or were turned away at the clinic. My colleagues and I spent the last few minutes of the session talking about disparities in access to health care. Reflecting on this session, I realized that my colleagues and I had spent almost an entire hour talking about history taking, physical examination, lab tests, and treatment options, when in reality, Iman and his family would probably not even have come into the clinic because they lacked health insurance coverage. Reaching a correct diagnosis and discussing comprehensive treatment options were irrelevant if Iman and many other refugees in similar situations could not access the health care that they needed. Perhaps we should have started the session with a discussion about Iman’s refugee status and how that affected his access to health care and treatment options. From this experience, I gleaned that diagnosis and treatment of disease cannot be separated from the social context of our patients’ lives. In addition to the scientific evidence and clinical principles that we need to consider, we must not forget to look at the whole patient and consider how social context can impact health. Moreover, I realized that understanding the social determinants of health can provide valuable information to meet our patients’ unique health needs. Educating our selves about our patients’ health insurance coverage, access to health care, and policy changes are just as important as learning about the underlying causes and management of disease. Finally, being a good physician means not only learning about our patients’ social context but also advocating for changes—from the clinic to the community level—that will allow our patients to access quality health care, regardless of where they are from. Matthew J. To, BMSc Mr. To is a medical student, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; e-mail: [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)0.003
metaresearch head score (Gemma)0.034
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 score0.572
Threshold uncertainty score0.611

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0060.006
Open science0.0020.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.5720.310

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 head0.071
GPT teacher head0.417
Teacher spread0.346 · 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
Published2014
Admission routes2
Has abstractyes

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