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Record W6991704909

Impacto en la calidad de vida y utilización de recursos sanitarios en pacientes con colostomia permanente

2017· dissertation· es· W6991704909 on OpenAlexaboutno aff

Bibliographic record

VenueRepositorio Institucional de la Universidad de Málaga (University of Málaga) · 2017
Typedissertation
Languagees
FieldMedicine
TopicStoma care and complications
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)Nursing careContext (archaeology)Quality (philosophy)Older people
DOInot available

Abstract

fetched live from OpenAlex

Objetivos:\n-Evaluar nivel de cuidados que se proporciona en nuestro entorno e identificar cual es el nivel de adherencia a las intervenciones enfermeras con más evidencia para el procedimiento de colostomía permanente que determinan las guías de practica clínica: Registered Nurses’ Association of Ontario (RNAO) \n-Identificar el impacto en la calidad de vida de la colostomía en el paciente y cómo influyen las intervenciones enfermeras en su modificación.\n-Estimar el uso de recursos sanitarios en nuestro entorno dimensionado en estancias y revisiones ambulatorias\n-Definir el perfil del cuidador principal , evaluando la sobre carga del mismo y cómo afecta esta situación en su entorno social.\nMetodología: Estudio observacional de cohorte retrospectivo en pacientes con colostomía permanente del Hospital Costa del Sol , después de un año de la realización y permanencia de la colostomia, mediante revisión de historia clínica y aplicando un instrumento de recolección de datos con entrevistas telefónicas al paciente, grabadas y entrevistas telefónicas grabadas, al cuidador principal.\nVariables: Calidad de vida, costes sanitarios, socio demográficas y clínicas.\nInstrumentos: Historia clínica, Registered Nurses’ Association of Ontario (RNAO). datos de actividad asistencial, Stoma-QOL (calidad de vida), Esfuerzo de cuidador.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.010
GPT teacher head0.278
Teacher spread0.268 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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