‘Patient satisfaction’: knowledge for ruling hospital reform — An institutional ethnography
Bibliographic record
Abstract
Driven by funding restraint, Canadian health-care has undergone over a decade of significant reform. Hospitals are being restructured, as text-based practices of accountability bring a new business-orientation into hospital and clinical management. New forms of knowledge, generated through records of various sorts, are a necessary resource for managing care in the new environment. This paper's research uses Canadian sociologist Dorothy E. Smith's institutional ethnographic methodology to critically analyse one instance of text-based management. I analyse information about 'patient satisfaction' as it is generated through a patient survey (in which I was implicated through my involvement with a hospitalized family member). Subsequently, I have studied the management environment into which that information would be entered. I argue that in the instance analysed, the information becomes part of a dominant consumer oriented healthcare discourse that subordinates concerns about 'what actually happened' as a professional caregiver would have known it. On this basis, I contend that this sort of taken-for-granted approach to making decisions about quality care in hospitals may be seriously, even dangerously, flawed.
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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".