Acting Slow in a Fast World: A Phenomenological Study of Caring in the Recovery Room
Bibliographic record
Abstract
In this paper, we discuss “the slow in the fast” related to care situations in a “fast-track” hospitalsetting were the length of patients’ stay has been reduced significantly. The discussion is basedon a narrative created from observations made in a postoperative care unit where patients areintensively observed and cared for during a very short time span.We found that within the phenomenological notions of lived time, lived space and livedillness, it is possible to create an imaginative space in time – to make a time warp. Despitebeing in a setting where the objective time measure dominates, the nurse can create a rhythmof her own in the room. Thus, acting slow in the quick meeting means that nurse-patientrelationship is characterized by calmness and quietness, the nurse’s engagement in the patient’ssuffering and her help to the patient to endure the present and hold the now.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".