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Record W2026608132 · doi:10.1080/10376178.2015.1011047

Night-time continence care in Australian residential aged care facilities: findings from a grounded theory study

2015· article· en· W2026608132 on OpenAlexaff
Joan Ostaszkiewicz, Bev OʼConnell, Trisha Dunning

Bibliographic record

VenueContemporary Nurse · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersAustralian Government
KeywordsAged careAuditMedicineNursingSleep (system call)Project commissioningHealth carePersonal careGrounded theoryQualitative researchFamily medicinePublishingBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Continence care commonly disrupts sleep in residential aged care facilities, however, little is known about what staff do when providing continence care, and the factors that inform their practice. AIMS: To describe nurses' and personal careworkers' beliefs and experiences of providing continence care at night in residential aged care facilities. METHODS/DESIGN: Eighteen nurses and personal careworkers were interviewed about continence care, and 24 hours of observations were conducted at night in two facilities. RESULTS/FINDINGS: Most residents were checked overnight. This practice was underpinned by staffs' concern that residents were intractably incontinent and at risk of pressure injuries. Staff believed pads protected and dignified residents. Decisions were also influenced by beliefs about limited staff-to-resident ratios. CONCLUSION: Night-time continence care should be audited to ensure decisions are based on residents' preferences, skin health, sleep/wake status, ability to move in bed, and the frequency, severity and type of residents' actual incontinence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.006
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.384
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2015
Admission routes1
Has abstractyes

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