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Record W4323050530 · doi:10.18502/kss.v8i4.12876

Calgary Family Intervention Model Approach to Improve Quality of Life for Diabetes Mellitus Patients

2023· article· en· W4323050530 on OpenAlexaboutno aff
Firda Laela Najah, Yudhi Permana, Andan Firmansyah, Heni Marliany, Henri Setiawan, Asri Aprilia Rohman, Nur Isriani Najamuddin, Marlina Indriastuti

Bibliographic record

VenueKnE Social Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)MedicineQuality of life (healthcare)NursingDiabetes mellitusFamily medicineDocumentationType 2 Diabetes MellitusQuality (philosophy)Family healthGerontologyPhysical therapy

Abstract

fetched live from OpenAlex

Calgary Family Intervention Model (CFIM) is a nursing care model that is dominated by the family and integrated with the nursing paradigm that focuses on families. A case study was conducted to determine the intervention to improve the quality of life for the patient with diabetes mellitus. Nursing care was carried out using the Calgary Family Intervention Model approach with the author for three days and continued by the family for two weeks. With data collection techniques include interviews, observation, physical examination, and documentation. The tools used in this case study are a set of physical examination tools, blood sugar check tools, a nursing kit, and the WHOQOL-BREF questionnaire. The results showed that Mr. J’s family had problems in the dimensions of physical and psychological health, with the established nursing diagnosis being a poor quality of life. In family members who experience diabetes mellitus with quality of life problems there are positive changes to the family and in handling, nutrition, exercise, foot care, and stress management for the patient. It can be seen from the results questionnaire which showed an improvement in scores than before, especially in the dimensions of physical and psychological health. Keywords: Calgary, diabetes mellitus, family, nursing, quality of life

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.110
GPT teacher head0.396
Teacher spread0.286 · 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
Published2023
Admission routes1
Has abstractyes

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