The bleary-eyed (but disciplined) patient: Hospital late night and early morning awakenings and Foucault’s disciplinary power in a healthcare panopticon
Bibliographic record
Abstract
It is an old adage that when one is staying at the hospital, a sleepless night will follow. Hospitals are notoriously difficult places to sleep in. This is due to a variety of factors, but one of the primary ones are the late night and early morning disturbances caused by nurses and other medical professionals, who are taking measurements and vital signs from the patient. Explaining why this occurs requires an understanding how the hospital is organized as a political institution. Using a Foucauldian theoretical framework, we critically examine the hospital as a disciplinary institution; we find that the hospital’s organizational impetus for a “perpetual examination” drives hospital sleeplessness. While the perpetual examination is not borne out of malice and is usually vital to the health of the patient, understanding this process helps us understand why hospitals are so difficult to sleep in. While we cannot reject the process of hospitalization wholesale, understanding what drives hospitals as political institutions helps us begin to improve them; helping patient sleep is one place to start.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.077 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".