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Record W2345682602 · doi:10.1177/0891241615625462

<i>Street Medicine</i> —Assessment Work Strategies of Paramedics on the Front Lines of Emergency Health Services

2016· article· en· W2345682602 on OpenAlexfundaboutno aff
Michael Corman

Bibliographic record

VenueJournal of Contemporary Ethnography · 2016
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsWork (physics)EthnographySituational ethicsHealth careFront (military)InterfacingSocial workSociologyFront lineEmergency medical servicesPublic relationsNursingPsychologyMedicineMedical emergencyPolitical scienceEngineeringSocial psychologyLaw

Abstract

fetched live from OpenAlex

This article is based on an institutional ethnographic inquiry into the work of paramedics and the institutional setting that organizes and coordinates their work processes in a major City in Canada. Drawing on more than two hundred hours of observations and more than one hundred interviews with paramedics (average length of 18 minutes) and other emergency medical personnel, this article explores the standard and not so standard work of paramedics as they assess and care for their patients on the front lines of emergency health services. The multiplicity of interfacing social, demographic, locational, and situational factors that shape and organize the work of paramedics are analyzed. In doing so, this article provides insights into the complex work of an understudied yet ever-important profession in health care.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.360
Teacher spread0.303 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations14
Published2016
Admission routes2
Has abstractyes

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