MétaCan
Menu
Back to cohort
Record W1994141723 · doi:10.1097/ans.0b013e31824fe6ae

Time to Disable the Labels That Disable

2012· article· en· W1994141723 on OpenAlexaff
Marion Alex, Joanne Whitty-Rogers

Bibliographic record

VenueAdvances in Nursing Science · 2012
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsHonorHarmReductionismEmpowermentDiversity (politics)SociologyPsychologyEconomic JusticeEpistemologySocial psychologyNursingMedicineLawPhilosophyInternet privacyPolitical science

Abstract

fetched live from OpenAlex

Nursing is grounded in communication with others, yet rarely are the words critiqued. Despite an ethical call to honor diversity, promote empowerment, and to do no harm, some of the language used in health care reflects historical prejudices, reductionism, and/or the overarching authority of medical or moral models. This article exposes some of the "harsh words" nurses sometimes unconsciously use, and it suggests alternatives. Influenced by an ethic of social justice and the ethic of relationship with others, an attempt will be made to explore nursing language with women and children. Implications for nursing philosophy and practice will be discussed.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.006

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 head0.053
GPT teacher head0.512
Teacher spread0.459 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Nursing ScienceSame topicObesity and Health PracticesFrench-language works237,207