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Record W4367722815 · doi:10.1177/23333936231170824

Elucidating the Ruling Relations of Nurses’ Work in Labor and Delivery: An Institutional Ethnography

2023· article· en· W4367722815 on OpenAlexafffund
Paula Kelly, Nicole Snow, Maggie Quance, Caroline Porr

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsMount Royal UniversityMemorial University of Newfoundland
FundersCanadian Nurses Foundation
KeywordsEthnographyHierarchyIdeologyWork (physics)MalpracticeSociologyObstetricsPolitical scienceLawNursingMedicinePoliticsAnthropology

Abstract

fetched live from OpenAlex

Obstetrics is a well-known area for malpractice and medical-legal claims, specifically as they relate to injuries the baby suffers during the intrapartum period. There is a direct implication for nurses' work in labor and delivery because the law recognizes that monitoring fetal well-being during labor is a nursing responsibility. Using institutional ethnography, we uncovered how two powerful ruling discourses, namely biomedical and medical-legal risk discourses, socially organize nurses' fetal surveillance work in labor and delivery through the use of an intertextual hierarchy and an ideological circle.

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)0.014
metaresearch head score (Gemma)0.018
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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.019
Scholarly communication0.0060.005
Open science0.0020.007
Research integrity0.0010.002
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 head0.446
GPT teacher head0.654
Teacher spread0.207 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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