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Record W2093828736 · doi:10.3928/0098-9134-20010701-10

Redesigning Nighttime Care FOR PERSONAL CARE RESIDENTS

2001· article· en· W2093828736 on OpenAlexaffabout
Deanne J O’Rourke, Kathleen Klaasen, Jeff A Sloan

Bibliographic record

VenueJournal of Gerontological Nursing · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsWinnipeg Regional Health Authority
Fundersnot available
KeywordsEveningBedtimePersonal careMorningMedicineSleep (system call)Long-term carePsychologyGerontologyNursingFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.463
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2001
Admission routes2
Has abstractyes

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