Bibliographic record
Abstract
Aims & Objectives: The primary objective was to examine overnight patient stimulation and current rest promotion practices in a tertiary care PICU. The secondary objective was to engage nurses in devising plausible strategies to optimize sleep and rest with the goal of developing a Clinical Guideline for our unit. Methods A 17-item bedside observational tool was devised with questions relating to patient demographics, acuity and presence of stimuli (lights, noise, procedures). On seven convenient nights over a six-week period, data were collected on every patient in PICU at three defined time points. Two focus groups concentrated on how PICU patients could be given the best opportunity to rest and experience restorative sleep. Results One hundred and thirty three overnight observations were collected. Most patients (70%) were infants, with an even split between cardiac and medical-surgical diagnoses. In a multivariate regression model, stimulation was associated with acuity (p<0.001) and younger age (p=0.001). Sleep promotion strategies were utilized most in high acuity patients, but still only rarely (20%). Nineteen nurses of varied experience participated in two focus groups. Themes that emerged included safety, unit culture, environment and family involvement. Conclusions All PICU patients are exposed to high levels of unprotected stimulation overnight, with younger and higher acuity patients at greatest risk. A Clinical Guideline is needed to standardize the way sleep and rest is promoted in PICU.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".