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Record W4283265400 · doi:10.1177/09697330221089093

Towards democratic institutions: Tronto’s care ethics inspiring nursing actions in intensive care

2022· article· en· W4283265400 on OpenAlexaff
Annie‐Claude Laurin, Patrick Martin

Bibliographic record

VenueNursing Ethics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStatus quoNursingContext (archaeology)PoliticsProfessionalizationPalliative careDemocratizationNursing careNursing ethicsDemocracySociologyPsychologyMedicinePolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Care as a concept has long been central to the nursing discipline, and care ethics have consequently found their place in nursing ethics discussions. This paper briefly revisits how care and care ethics have been theorized and applied in the discipline of nursing, with an emphasis on Tronto's political view of care. Adding to the works of other nurse scholars, we consider that Tronto's care ethics is useful to understand caring practices in a sociopolitical context. We also contend that this vision can be used specifically to politicize nurses, by encouraging them to think critically about the context in which they work and how they can participate to change the status quo, notably by prompting the democratization of care in institutional settings. We illustrate this by demonstrating how moral distress that can occur with aggressive or futile treatments in the intensive care unit can be reduced if nurses are systematically included in the decision-making process. By showing some ways in which nursing political actions can begin to change the status quo as it pertains to futile treatments at the end of life, we can help empower nurses to strive to be included in political spaces and voice their concerns to have their professional needs met.

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)0.008
metaresearch head score (Gemma)0.075
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.075
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0130.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.069
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 head0.456
GPT teacher head0.601
Teacher spread0.145 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2022
Admission routes1
Has abstractyes

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