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Record W3178327544 · doi:10.30886/estima.v19.1025_in

PERCEPTIONS OF COLOSTOMY PATIENTS ABOUT NURSING CARE IN ONCOLOGY INPATIENT UNITS

2021· article· en· W3178327544 on OpenAlexaff
Cláudia Bruna Perin, Andréia Machado Cardoso, Alessandra Yasmin Hoffmann, Vanessa Zancanaro, Vanessa Manfrin

Bibliographic record

VenueRevista Estima · 2021
Typearticle
Languageen
FieldMedicine
TopicStoma care and complications
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsColostomyMedicineNursingNursing careStoma (medicine)Qualitative researchColorectal cancerFamily medicineCancerInternal medicineGeneral surgery

Abstract

fetched live from OpenAlex

Objective: To analyze the perceptions of colorectal cancer patients using colostomy on the nursing care of the oncology inpatient units of a hospital in western Santa Catarina. Methods: Descriptive-exploratory study of qualitative approach carried out in the oncology inpatient units of the Hospital Regional do Oeste, in the period from January to August 2020, through a questionnaire containing sociodemographic data and semi-structured interview, applied to 20 patients with colorectal cancer using colostomy. The data were analyzed using Laurence Bardin’s Content Analysis. Results: The results indicated a prevalence of colostomy male patients, with a mean age of 60.25 years, married, retired, and with incomplete elementary education. From the qualitative analysis of the interviews emerged the category: patients’ perceptions of nursing care, which was subdivided into: nursing care with the bag and the stoma and nursing care during hospitalization. Conclusion: At the end of the survey, it is concluded that the colostomy patients perceive that the nursing staff performs the essential care of the bag and the stoma, including its exchange and hygiene during hospitalization, meeting the patients’ needs. In addition, they provide important guidance on the use of the devices, promoting health education.

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 score0.464
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.349
Teacher spread0.326 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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