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Record W1988772747 · doi:10.1097/won.0b013e3181b41301

Clean Intermittent Catheterization

2009· article· en· W1988772747 on OpenAlexaff
Gisele Martins, Zaida Aurora Sperli Geraldes Soler, Fernando Batigália, Katherine Moore

Bibliographic record

VenueJournal of Wound Ostomy and Continence Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineOutpatient clinicPortugueseTeaching hospitalNursingFamily medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to propose a systematic teaching and learning strategy for Brazilian caregivers of children with neurogenic bladder dysfunction(NBD), by using an illustrated booklet written in Portuguese. DESIGN: Descriptive study. SUBJECTS AND SETTING: Caregivers of children requiring clean intermittent catheterization (CIC) were approached when attending the pediatric urology outpatient clinic of Hospital de Base in Sao Jose do Rio Preto city, Brazil. METHODS: After educational sessions, a supervised procedure was done, with the child's caregiver observing the technique. RESULTS: Twenty-three caregivers of children with NBD provided feedback on a CIC teaching booklet. The children were all cared for at the pediatric urology outpatient clinic of a teaching hospital in Sao Jose do Rio Preto city, Brazil. The booklet was evaluated as "excellent" concerning organization and the quality of the illustrations by the majority of the caregivers. All caregivers stated that they had developed the ability to perform CIC successfully; 61% evaluated their learning process as "excellent," whereas 39% evaluated it as "good." CONCLUSION: The booklet successfully reached the goals and now is implemented in orientations about CIC.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0080.001

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 head0.017
GPT teacher head0.322
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations20
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Wound Ostomy and Continence NursingSame topicUrinary Bladder and Prostate ResearchFrench-language works237,207