Nurses and electronic health records in a Canadian hospital: examining the social organisation and programmed use of digitised nursing knowledge
Bibliographic record
Abstract
Institutional ethnography (IE) is used to examine transformations in a professional nurse's work associated with her engagement with a hospital's electronic health record (EHR) which is being updated to integrate professional caregiving and produce more efficient and effective health care. We review in the technical and scholarly literature the practices and promises of information technology and, especially of its applications in health care, finding useful the more critical and analytic perspectives. Among the latter, scholarship on the activities of economising is important to our inquiry into the actual activities that transform 'things' (in our case, nursing knowledge and action) into calculable information for objective and financially relevant decision-making. Beginning with an excerpt of observational data, we explicate observed nurse-patient interactions, discovering in them traces of institutional ruling relations that the nurse's activation of the EHR carries into the nursing setting. The EHR, we argue, materialises and generalises the ruling relations across institutionally located caregivers; its authorised information stabilises their knowing and acting, shaping health care towards a calculated effective and efficient form. Participating in the EHR's ruling practices, nurses adopt its ruling standpoint; a transformation that we conclude needs more careful analysis and debate.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.031 | 0.026 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".