A discursive exploration of the practices that shape and discipline nurses’ responses to postoperative delirium
Bibliographic record
Abstract
A discursive exploration of the practices that shape and discipline nurses' responses to postoperative delirium Although delirium is classified as a medical emergency, it is often not treated as such by health care providers. The aim of this study was to critically examine, through a poststructural, Foucauldian concept of discourse, the language practices and discourses that shape and discipline nurses' care of older adults with postoperative delirium (POD) with a purpose to question accepted nursing practice. The study was based on data collected from face-to-face, in-depth, personal interviews with six nurses who work on an acute postoperative patient care unit. Five analytic readings of the data identified two prominent discourses at work in nursing practice which influenced the care of patients with POD. These were identified as discourses of legitimacy/illegitimacy and discourses of nursing work. Through the process of poststructural analysis it became evident that one overriding discourse - the biomedical/scientific discourse - served to direct, legitimize and govern all other discourses. The findings of this study have implications for nursing knowledge and practice, length of hospital stay and improved patient outcomes. This study builds on previous work and is the first study to conduct a discourse analysis illuminating nurses' responses to POD through comparison with other acute medical emergencies from a poststructural perspective.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".