Non-pharmacologic interventions to improve sleep of medicine inpatients: a controlled study
Bibliographic record
Abstract
Background: Sleep quality in hospitalized medicine patients is poor, with environmental factors among the most frequently cited reasons.Objective: We tested the efficacy of a non-pharmacologic intervention on the sleep quality of medicine inpatients.Design/Methods: A controlled study to evaluate our non-pharmacologic multidisciplinary ‘TUCK-in’ protocol (which includes timed lights-off periods, minimizing night-time noise, distribution of earplugs at bedtime, cued toileting before bedtime, and identification and reduction of modifiable interruptions) was deployed on two of five identical medicine wards. Randomization was at the level of the ward.The main outcome measure was self-reported duration of night-time sleep within 48 hours prior to discharge. Additional outcome measures included the Verran–Snyder-Halpern (VSH) Sleep Score and inpatient sleep pharmaceutical use.Results: Self-reported duration of night-time sleep (median 5.0 vs. 5.0 hours, p = 0.29) and daytime sleep (1.0 versus 0.5 hours, p = 0.43) did not differ between the 40 intervention patients and the 41 control patients (p = 0.13 on multivariate analysis). Cumulative VSH sleep disturbance (median 420 versus 359, p = 0.19), efficacy (median 169 versus 192, p = 0.29), or supplementation (median 97 versus 100, p = 0.51) scales were also not different between study arms.Conclusions: Although staff reported the protocol to be achievable and worthwhile, there were no significant differences in any of the outcomes between intervention and control patients.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".