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Record W2620758620 · doi:10.1111/nin.12201

Instrumentalisation of the health system: An examination of the impact on nursing practice and patient autonomy

2017· article· en· W2620758620 on OpenAlexaff
Jesús Molina‐Mula, Elizabeth Peter, Julia Gallo‐Estrada, Catalina Perelló‐Campaner

Bibliographic record

VenueNursing Inquiry · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutonomyManagerialismInstitutionHealth careNursingResistance (ecology)PsychologySociologyMedicinePublic relationsPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

Most current management systems of healthcare institutions correspond to a model of market ethics with its demands of competitiveness. This approach has been called managerialism and is couched in terms of much-needed efficiencies and effective management of budgetary constraints. The aim of this study was to analyse the decision-making of nurses through the impact of health institution management models on clinical practice. Based on Foucault's ethical theory, a qualitative study was conducted through a discourse analysis of the nursing records in a hospital unit. The results revealed that the health institution standardises health care practice, which has an impact on professional and patient autonomy as it pertains to decision-making. The results of this research indicate that resistance strategies in the internal structures of health organisations can replace the normalisation and instrumentalisation of professional practice aimed at promoting patient self-determination.

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)0.039
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 score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.023
Scholarly communication0.0070.004
Open science0.0010.010
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 head0.150
GPT teacher head0.523
Teacher spread0.373 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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