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Record W2767858659 · doi:10.1177/1049732317739266

Titrating the Rig: How Paramedics Work in and on Their Ambulance

2017· article· en· W2767858659 on OpenAlexaff
Michael Corman

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsWork (physics)SituatedUnit (ring theory)Quality (philosophy)Health careEthnographyMedical emergencyOccupational safety and healthNursingRelation (database)Emergency medical servicesAmbulance servicePsychologyPublic relationsMedicineSociologyEngineeringPolitical scienceComputer science

Abstract

fetched live from OpenAlex

In this article, I take readers inside of an ambulance and explore how paramedics work in and on their "apparatus unit" to make it a workable fit. This taken-for-granted work is important because much is at stake in the back of the ambulance, particularly in relation to quality of care and safety. I draw on data from an institutional ethnography into the socially organized work and work settings of paramedics, which included more than 200 hr of observations and more than 100 interviews with paramedics. The findings shed light on the situated work processes of paramedics as they orient and respond to their "apparatus unit" and enact quality and safety in practice. This article adds to the sociological literature on work and occupations as well as safety and quality in health care of an increasingly important group of health care and emergency services professional.

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)0.010
metaresearch head score (Gemma)0.028
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.016
Scholarly communication0.0080.006
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.742
GPT teacher head0.692
Teacher spread0.050 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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