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Record W4284892838 · doi:10.7759/cureus.26649

Patient, Family, and Peer Engagement in Nursing Care as an Effort to Improve the Functional Independence of Post-stroke Urinary Incontinence Patients: A Cross-Sectional Study

2022· article· en· W4284892838 on OpenAlexaff
Heltty Heltty

Bibliographic record

VenueCureus · 2022
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineUrinary incontinenceFunctional Independence MeasureQuality of life (healthcare)Cross-sectional studyPhysical therapyDescriptive statisticsNursingFamily medicineActivities of daily livingSurgery

Abstract

fetched live from OpenAlex

Introduction The engagement of patients, family members, and peers is one approach that can be taken to improve the patient's health status. This study aimed to investigate the relationship between patient, family, and peer involvement in nursing care to improve the functional independence of post-stroke urinary incontinence (UI) patients. Methods This cross-sectional descriptive design study was conducted in three hospitals in Kota Kendari, Sulawesi Tenggara, Indonesia. A total of 104 patients were selected using a simple random sampling method. Data were collected during the research period through a survey and observation. Data were analyzed using descriptive analysis and the Mann-Whitney test. Results There was a statistically significant difference in the motor subscale of the Functional Independence Measure (motor-FIM) domain (p<0.05). Based on the results of the analysis, there was a relationship between each motor-FIM domain in the engagement group. Conclusions The involvement of patients, families, and peers in patient care needs to be comprehensively improved in an effort to increase patient independence, which in turn can improve the quality of life of post-stroke urinary incontinence patients.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.303
Teacher spread0.288 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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