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Record W3008498410 · doi:10.1111/nin.12346

How nurses’ use of language creates meaning about healthcare users and nursing practice

2020· article· en· W3008498410 on OpenAlexaff
Sherry Dahlke, Kathleen F. Hunter

Bibliographic record

VenueNursing Inquiry · 2020
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHealth careMeaning (existential)Context (archaeology)NursingPower (physics)PsychologySociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Nursing practice occurs in the context of conversations with healthcare users, other healthcare professionals, and healthcare institutions. This discussion paper draws on symbolic interactionism and Fairclough's method of critical discourse analysis to examine language that nurses use to describe the people in their care and their practice. We discuss how nurses' use of language constructs meaning about healthcare users and their own work. Through language, nurses are articulating what they believe about healthcare users and nursing practice. We argue that the language nurses use can contribute to viewing their practice as tasks on bodies that must be accomplished efficiently and objectively within the biomedical model, rather than relational and person-centered. Moreover, the language nurses use can perpetuate a sense of powerlessness within healthcare systems yet paradoxically they are in a position of power over healthcare users. Nurses' compliance with the efficiency and biomedical model results in a lack of emphasis on the full breadth of nursing work, which could be enacted in relational rather than power-laden practices. We conclude by positing that careful use of language among nurses in all settings is essential, if we are to begin to articulate what nursing is to ourselves and to others.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.074
GPT teacher head0.375
Teacher spread0.301 · 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.

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

Citations13
Published2020
Admission routes1
Has abstractyes

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