Nurses’ knowledge and deliberations crucial to Barcoded Medication Administration technology in a Dutch hospital: Discovering nurses’ agency inside ruling
Bibliographic record
Abstract
This article shows how Barcoded Medication Administration technology institutionally organizes and rules the daily actions of nurses. Although it is widely assumed that Barcoded Medication Administration technology improves quality and safety by reducing the risk of human error, little research has been done on how this technology alters the work of nurses. Drawing on empirical and conceptual strategies of analysis, this qualitative study used certain tools of institutional ethnography to provide a view of how nurses negotiate Barcoded Medication Administration technology. The approach also uses elements from practice theory in order to discern how technology operates as a player on the field instead of being viewed as a 'mere' tool. A literature review preceded participant observation, whereby 17 nurses were followed and data on an orthopaedic ward were collected over a period of 9 months in 2011 and 2012. Barcoded Medication Administration technology relies on nurses' knowledge to mediate between the embedded logics of its design and the unpredictable needs of patients. Nurses negotiate their own professional logic of care in the form of moment-to-moment deliberations which subvert the ruling frame of the barcoded system and its objectified model of patient safety. The logic of Barcoded Medication Administration technology differs from the logic of nursing care, as this technology presumes medication distribution to be linear, even though nurses follow another line of actor-bound safety practices that we characterize as 'deliberations'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".