Nursing Practices to Detect Acute Delirium, Safeguard Patients Experiencing Acute Delirium, and Help Reduce or Eliminate Acute Delirium
Bibliographic record
Abstract
Acute delirium is very common among hospital patients, particularly older patients. Nurses have a major role inthe care of these patients, yet there are no evidence-based nursing care guidelines to help nurses detect patientswho are experiencing acute delirium, safeguard them, and assist their recovery. This study sought to identify andprioritize nursing practices for detecting these patients, safeguarding them, and assisting their recovery fromacute delirium. A two-stage voluntary paper Delphi survey was used for this purpose. This study targeted allnurses who worked on adult medical/surgical units at two full-service acute care hospitals in Western Canadawho had cared for a patient diagnosed with acute delirium in the past 12 months. The first survey revealed manynursing practices exist to detect, safeguard, and assist recovery. The second revealed one preferred practice andfour others for each of the following: Detecting acute delirium, safeguarding patients, and helping patientsrecover. Research is now needed to establish if these constitute “best practice” nursing care for enhanced patientoutcomes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".