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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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
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 teacher head, 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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