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Configurations of power relations in the Brazilian emergency care system: analyzing a context of visible practices

2012· article· en· W1875856147 on OpenAlexaff
Isabela Silva Câncio Velloso, Christine Ceci, Marília Alves

Bibliographic record

VenueNursing Inquiry · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArgument (complex analysis)Context (archaeology)Power (physics)Work (physics)VisibilityHealth careSociologyDisciplinePsychologyMedicinePolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

In this paper, we make explicit the changing configurations of power relations that currently characterize the Brazilian Emergency Care System (SAMU) team in Belo Horizonte, Brazil. The SAMU is a recent innovation in Brazilian healthcare service delivery. A qualitative case study methodology was used to explore SAMU's current organizational arrangements, specifically the power relations that have developed and that demonstrate internal team struggles over space and defense of particular occupational interests. The argument advanced in this paper is that these professionals are developing their work in conditions of exposure, that is, they are always being observed by someone, and that such observational exposure provides the conditions whereby everyday emergency care practices are enacted such that practice is shaped by, as well as shapes, particular, yet recognizable power relationships. Data were collected through the observation of the SAMU's work processes and through semi-structured interviews. Research materials were analyzed using discourse analysis. In the emergency care process of work, visibility is actually embedded in the disciplinary context and can thus be analyzed as a technique applied to produce disciplined individuals through the simple mechanisms elaborated by Foucault such as hierarchical surveillance, normalizing judgment, and the examination.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.152
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.470
Teacher spread0.365 · 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 teacher head, 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

Citations15
Published2012
Admission routes1
Has abstractyes

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