Maintaining a medical institution in a context of materiality change: Lessons from a Canadian university hospital
Bibliographic record
Abstract
This research aimed to better understand how institutions are maintained, and the role of materiality in this institutional work. More specifically, the present qualitative case study analyzed how different actors in a large academic hospital in Canada worked together (i.e. accomplished institutional work) to maintain the institution of medical record keeping as a new clinical information system (computerized physician order entry-the material entity) was enacted. The study reveals that, to maintain the institution at stake, the intertwinement of processes of creating and maintaining institutions took place. In fact, different forms of institutional work interact Results also strongly suggest that the design of computerized physician order entry and its implementation (i.e. the materiality involved in this institutional change) played an important role in the maintenance of the institution of medical record keeping: on the one hand, it was particularly present in three types of institutional work, namely enabling, policing, and deterring; on the other hand, it appeared to be an essential component of the routinization of work by allowing a better fit between the new technology and the organization of work.
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 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.048 | 0.031 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".