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Record W4323979904 · doi:10.18192/aporia.v15i1.6503

Ending the invisible work of nurses: Reflection following a study about nursing support of relatives

2023· article· en· W4323979904 on OpenAlexaffvenue
Stéphanie Daneau, Anne Bourbonnais, Alain Legault

Bibliographic record

VenueAporia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsUniversité de MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsDenialInvisibilityObedienceNursingWork (physics)Reflection (computer programming)PsychologyPhenomenonMedicineSocial psychologyEpistemologyComputer scienceArtificial intelligencePsychotherapist

Abstract

fetched live from OpenAlex

Formerly playing an assistive role motivated by virtue, obedience, and self-denial, nurses are now autonomous professionals with unique expertise, skills, and competencies. However, current recognition of the nursing role is far from optimal and perpetuates the invisibility of nurses’ real work. This article examines one component of the situation: the non-recognition of the nursing role by nurses themselves. Hypotheses explaining this phenomenon, like the history of nursing and the lack of common language, is presented, as well as its impacts, followed by exploration of possible solutions.

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)0.017
metaresearch head score (Gemma)0.060
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 score0.025
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0250.026
Scholarly communication0.0110.008
Open science0.0040.012
Research integrity0.0110.020
Insufficient payload (model declined to judge)0.0020.001

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 head0.131
GPT teacher head0.478
Teacher spread0.348 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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