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Record W2546602784 · doi:10.21013/jmss.v5.n1.p11

Quality of life of Persons with Diabetes Mellitus

2016· article· en· W2546602784 on OpenAlexfundno aff
Esther Oommen, Mathew Cp

Bibliographic record

VenueIRA-International Journal of Management & Social Sciences (ISSN 2455-2267) · 2016
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsNonprobability samplingDiabetes mellitusQuality of life (healthcare)GerontologyPopulationMedicineBlood sugarPsychologyEnvironmental healthEndocrinologyNursing

Abstract

fetched live from OpenAlex

Quality of life is considered as the extent to which a person appreciates the essential potential outcomes of his/her life. Potential outcomes result from the open doors and confinements every individual has in his/her life and mirror the cooperation of individual and ecological components. The main aim of this study was to understand the quality of life among the people with diabetes mellitus. The sampling technique used for this study was purposive sampling. WHOQOL- BREF questionnaire was used to assess the quality of life. It was assessed through four domains like physical, psychological, social and environment. Through results it was found that high percentage of study population had low quality of life in all four domains. The researcher also assessed the management of the illness in the people diagnosed with the diabetes mellitus. A self -structured questionnaire was used for this purpose. The results showed that the study population manage their illness through various measures like diet restrictions, follow ups with the doctor, maintained optimal blood sugar, treatment adherence and involve in exercise.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.181
GPT teacher head0.483
Teacher spread0.302 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueIRA-International Journal of Management & Social Sciences (ISSN 2455-2267)Same topicArtificial Intelligence in HealthcareFrench-language works237,207