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Record W2810760000 · doi:10.1111/nup.12215

Embracing the wild profusion: A Foucauldian analysis of the impact of healthcare standardization on nursing knowledge and practice

2018· article· en· W2810760000 on OpenAlexaffabout
Allie Slemon

Bibliographic record

VenueNursing Philosophy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStandardizationTriageHealth careNursingContext (archaeology)PsychologyMedicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Standardization has emerged as the dominant principle guiding the organization and provision of healthcare, with standards resultantly shaping how nurses conceptualize and deliver patient care. Standardization has been critiqued as homogenizing diverse patient experiences and diminishing nurses' skills and critical thinking; however, there has been limited examination of the philosophical implications of standardization for nursing knowledge and practice. In this manuscript, I draw on Foucault's philosophy of order and categorization to inform an analysis of the consequences of healthcare standardization for the profession of nursing. I utilize three exemplars to illustrate the impact of the primacy of standardized thinking and practices on nurses, patients and families: pain assessments using the 0-10 pain scale; patient triage emergency departments through the Canadian Triage and Acuity Scale; and determination of cause of death within the context of the current opioid crisis. Through each exemplar, I demonstrate that standardization reductively constrains nursing knowledge and the health and healthcare experiences of patients and populations. I argue that the centrality of standardization must be re-envisioned to embrace the complexity of health and more effectively and meaningfully frame nursing knowledge and practice within healthcare systems.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.001
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 head0.403
GPT teacher head0.665
Teacher spread0.262 · 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 designObservational
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
Published2018
Admission routes2
Has abstractyes

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