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Record W2972270949 · doi:10.1111/nup.12282

Complicating nursing's views on religion and politics in healthcare

2019· article· en· W2972270949 on OpenAlexafffund
Sheryl Reimer‐Kirkham

Bibliographic record

VenueNursing Philosophy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsTrinity Western UniversityWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPoliticsSecularismHealth carePower (physics)RealmSociologyNursingPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

Nursing, with its socially embedded theory and practice, inevitably operates in the realm of power and politics. One of these political sites is that of religion, which to varying degrees continues to shape beliefs about health and illness, the delivery of healthcare services and the nurse-patient encounter. In this paper, I attempt to complicate nursing's views on religion and politics in healthcare, with the intent of thinking critically and philosophically about questions that arise at the intersection of religion, politics and nursing/healthcare. These questions include the following: What is the domain of religion and politics? How (non)religious are the contemporary societies in which nurses practice? What are the variations and implications of secularism? How is religion entangled with other intersecting social relations of power? How does a political reading of religion and politics matter to the concerns of nursing?

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)0.018
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.060
Scholarly communication0.0110.010
Open science0.0020.009
Research integrity0.0050.011
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 head0.060
GPT teacher head0.370
Teacher spread0.310 · 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 designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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