Redesigning Nighttime Care FOR PERSONAL CARE RESIDENTS
Bibliographic record
Abstract
This study investigated the effects of non-disruptive nighttime care for residents in a personal care setting. The sample consisted of 18 personal care home residents in an urban, 388-bed, long-term care facility located in Winnipeg, Manitoba, Canada. The study used a quasi-experimental, single-arm design, exposing all residents to both intervention and control conditions. Independent variables were the current nighttime routine of regular rounds to turn and change residents, and a non-disruptive plan of care in which residents were checked hourly by staff and necessary care was provided when they were awake. Outcome variables included total sleep from evening bedtime to morning awakening, longest period of uninterrupted sleep at night, amount of time spent sleeping during the day, self-reported restfulness of cognitively intact residents, and skin condition. Findings suggested that the non-disruptive nighttime care routine increased total sleep by an average of 30 minutes a night for each resident. The amount of uninterrupted sleep increased by approximately 45 minutes with the new routine. No significant differences were noted in the amount of time spent sleeping during the day. There was no evidence of skin breakdown during any phase of the study. Clinical implications of this study demonstrate a need for gerontological nurses to re-evaluate nighttime care routines in personal care settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".