Maths for medications: an analytical exemplar of the social organization of nurses' knowledge
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".