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Record W3034698147 · doi:10.7326/m20-0151

Vicarious Trauma: The Hazard and Joy of Caring for Refugees

2020· review· en· W3034698147 on OpenAlexaffabout
Gabriel E. Fabreau

Bibliographic record

VenueAnnals of Internal Medicine · 2020
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRefugeeMedicineCrueltyMedia studiesCriminologyPsychologySociologyHistory

Abstract

fetched live from OpenAlex

On Being a Doctor16 June 2020Vicarious Trauma: The Hazard and Joy of Caring for RefugeesGabriel E. Fabreau, MD, MPHGabriel E. Fabreau, MD, MPHUniversity of Calgary, Calgary, Alberta, Canada (G.E.F.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M20-0151 Audio Reading - “Vicarious Trauma: The Hazard and Joy of Caring for Refugees” Audio. Michael A. Lacombe, MD, Annals Associate Editor, reads “Vicarious Trauma: The Hazard and Joy of Caring for Refugees” by Gabriel E. Fabreau, MD, MPH Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail I knew I was in trouble when I found myself crying inexplicably at a stoplight or in the hospital on teaching rounds. The chilling stories from refugees prompted ever more hideous nightmares. Most troubling, the optimism that had fueled my passion for refugee health was becoming eclipsed by a disillusionment with humanity.“Why do we try so hard to help people heal and recover when humans themselves are capable of such cruelty?” I wondered.At the Mosaic Refugee Health Clinic—a specialized, multidisciplinary clinic that cares for recently arrived refugees and asylum seekers in Calgary—there is a look I have seen ... Author, Article, and Disclosure InformationAffiliations: University of Calgary, Calgary, Alberta, Canada (G.E.F.)Corresponding Author: Gabriel Fabreau, MD, MPH, TRW 3E28, 3280 Hospital Drive NW, Calgary, AB T2T4Z6, Canada; e-mail, [email protected]ca. PreviousarticleNextarticle Advertisement Audio Reading - “Vicarious Trauma: The Hazard and Joy of Caring for Refugees” Audio. Michael A. Lacombe, MD, Annals Associate Editor, reads “Vicarious Trauma: The Hazard and Joy of Caring for Refugees” by Gabriel E. Fabreau, MD, MPH Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ FiguresReferencesRelatedDetails Metrics 16 June 2020Volume 172, Issue 12Page: 830-831KeywordsEmotionsFatigueFearGratitudeHealth care providersMemoryNauseaRefugee healthSafetySuicide ePublished: 16 June 2020 Issue Published: 16 June 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...

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)0.002
metaresearch head score (Gemma)0.015
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: Review · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0400.009

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 head0.134
GPT teacher head0.469
Teacher spread0.335 · 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
GenreReview

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

Citations4
Published2020
Admission routes2
Has abstractyes

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