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Record W3211096399 · doi:10.1177/23337214211046090

Understanding Nursing Knowledge of Continence Care and Bladder Scanner Use in Long-Term Care: An Evaluation Study

2021· article· en· W3211096399 on OpenAlexafffund
Tracy Christianson, Tracy J. Hoot, Melanie Todd

Bibliographic record

VenueGerontology and Geriatric Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsThompson Rivers University
FundersCanadian Institutes of Health Research
KeywordsMedicineNursingUrinary continenceLong-term careUrinary incontinenceNursing careUrologyInternal medicine

Abstract

fetched live from OpenAlex

Because urinary continence is an essential indicator for quality of life for older adults in long-term care, it is important to accurately assess and treat those at risk for incontinence. This evaluation study was to increase understanding of the issues related to the implementation of bladder scanners while exploring nursing staff knowledge about continence care of older adults in long-term care settings. Using a mixed-methods design, nursing care staff (RN, LPN, Care Aide) at six long-term care homes completed a bladder care knowledge survey and participated in focus group discussions to explore continence care knowledge and use of bladder scanners to manage continence issues. Twenty-eight nursing care staff participated; findings showed continence care knowledge varied by profession, and the use of portable bladder scanners is affected by knowledge, training, and scopes of practice. Going forward, exploring scopes of practice and education are needed for effective assessment, management, and treatment of continence.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.378
Teacher spread0.269 · 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 designObservational
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

Citations5
Published2021
Admission routes2
Has abstractyes

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