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Self-Efficacy, Self-Care Activities Management and Treatment Outcome among Type 2 Diabetes Saudi Patients: Across-Sectional Study

2017· article· en· W2611804477 on OpenAlexvenueno aff
Abubaker Ibrahim Elbur, Abdullah Salem AlMalki, Sultan A. Alharthi

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusCross-sectional studyType 2 diabetesSelf-managementDiabetes managementFamily medicineGerontologyEndocrinology

Abstract

fetched live from OpenAlex

The main aims of this study were to measure the level of diabetes management self-efficacy and adherence to self-care activities management and to identify the influence of self- efficacy on management of self-care activities and the outcome of treatment among type 2 diabetic patients. A cross-sectional study was conducted at the Diabetes Clinic; King Faisal Medical Complex during the period of June –August 2016. Patients aged >20 years and diagnosed with type 2 diabetes for at least one year. Data was collected through face-to-face interview method using a pretested and validated questionnaire. Data was processed by the software SPPS (21.0 SPSS Inc., Chicago IL, USA). P value

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.388
Teacher spread0.333 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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