Bibliographic record
Abstract
AIMS: The aim of this study is to provide an overview of the way that medication work is conceptualised in the literature and introduce a cautionary note into the enthusiasm with which nurses' medication work is currently being reformed. BACKGROUND: This article is an overview outlining how nurses' medication work is discursively organised and known about authoritatively. There is a great deal of safety and coordination work that nurses undertake in relation to the 'task' of administering medications that remains hidden and misunderstood in the dominant ways that knowledge about medication administration is structured. Technological innovations are eagerly embraced, but we posit that these strategies may be having an impact on nursing work with medications in unknown and potentially deleterious ways. We develop a critical analysis of the discursively limiting frameworks of biomedical science, law, management and safety through which nurses' medication practices are currently framed. This analysis provides insight into the ideologies and power relations that are taken up by nurses. The analytic work presented here informs an institutional ethnographic study that, at the time of this publication, is currently in the first stage of data collection. The study promises to contribute new knowledge about medication work as nurses know it. DESIGN: A discursive paper including a selective critical analysis of the descriptive, theoretical and empirical literature. METHODS: Databases were searched using the keywords medication work, medication administration and medication error. CONCLUSION: The enthusiasm with which technological innovations are being introduced must be tempered with equal attention to the impact that this has on nursing work. RELEVANCE TO CLINICAL PRACTICE: The way that medication work is conceptualised is constraining and covers over much of what actually happens in everyday nursing practice. Technological innovations change practice, often in unintended or unknown ways. Explicating the actuality of medication work offers an enhanced understanding and allows nurses to appraise critically the ways their work has been transformed.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".