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Maths for medications: an analytical exemplar of the social organization of nurses' knowledge

2011· article· en· W2164063403 on OpenAlexaff
Louise Dyjur, Janet Rankin, Annette Lane

Bibliographic record

VenueNursing Philosophy · 2011
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsAlberta Bible CollegeUniversity of Calgary
Fundersnot available
KeywordsMateriality (auditing)NumeracyEthnographyArgument (complex analysis)Objectivity (philosophy)PsychologyParticipant observationPedagogyNursingSociologyEpistemologyMedicineLiteracySocial science

Abstract

fetched live from OpenAlex

Within the literature that circulates in the discourses organizing nursing education, there are embedded assumptions that link student performance on maths examinations to safe medication practices. These assumptions are rooted historically. They fundamentally shape educational approaches assumed to support safe practice and protect patients from nursing error. Here, we apply an institutional ethnographic lens to the body of literature that both supports and critiques the emphasis on numeracy skills and medication safety. We use this form of inquiry to open an alternate interrogation of these practices. Our main argument posits that numeracy skills serve as powerful distraction for both students and teachers. We suggest that they operate under specious claims of safety and objectivity. As nurse educators, we are captured by taken-for-granted understandings of practices intended to produce safety. We contend that some of these practices are not congruent with how competency actually unfolds in the everyday world of nursing practice. Ontologically grounded in the materiality of work processes, we suggest there is a serious disjuncture between educators' assessment and evaluation work where it links into broad nursing assumptions about medication work. These underlying assumptions and work processes produce contradictory tensions for students, teachers and nurses in direct practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.070
GPT teacher head0.364
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

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