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Record W2138277966 · doi:10.5430/jnep.v5n11p73

Self-management and family support in chronic diseases

2015· article· en· W2138277966 on OpenAlexvenueno aff
María Isabel Peñarrieta, Florabel Flores Barrios, Tranquilina Gutiérrez Gómez, Socorro Piñones-Martínez, Eunice Reséndiz-González, Luz María Quintero-Valle

Bibliographic record

VenueJournal of Nursing Education and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsStatistical significanceFamily supportDiabetes mellitusKruskal–Wallis one-way analysis of varianceMedicineTest (biology)Self-managementScale (ratio)Statistical analysisClinical psychologyPsychologyInternal medicinePhysical therapyMann–Whitney U testStatisticsEndocrinology

Abstract

fetched live from OpenAlex

Objective : The p ur pose was to evaluate the behavior of self-management in people with: diabetes, hypertension and cancer, and to analyze the relationship between self-management and family support. Methods : This study has cross-sectional and correlational design. A convenience sample was used. The study was conducted at the Sanitary District Number 2 of Tampico, Tamaulipas, México. The sample consisted of 299 patients, the scale of self-management in chronic illness: “Partners in Health Scale”. The Kruskal-Wallis test, the Spearman and Kendall-Tau correlation were used for the analysis. Results and Conclusions : The people of the study showed poor self-management. The statistical significance was found in the adherence dimension, being this difference in the group which was diagnosed with cancer, vs the diabetes and hypertension groups. The results of the family APGAR showed that 25% of the participants had moderate and severe family dysfunction; the results also show that this family support is not the only factor to consider in this behavior, although the statistical results were significant, yet this relationship is medium or low.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.186

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 communication0.0000.001
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 head0.061
GPT teacher head0.405
Teacher spread0.344 · 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 designNot applicable
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

Citations24
Published2015
Admission routes1
Has abstractyes

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