MétaCan
Menu
Back to cohort
Record W2610726168 · doi:10.4103/ijem.ijem_52_17

Diabetes care: Inspiration from Sikhism

2017· review· en· W2610726168 on OpenAlexaff
Sanjay Kalra, Gagan Priya, InderpreetKaur Dardi, Simarjeet S. Saini, Sameer Aggarwal, Ramanbir Singh, Harpreet Kaur, Gurinder Singh, Vipin Talwar, Parminder Singh, JS Saini, Sandeep Julka, Rajeev Chawla, Sarita Bajaj, Singh Devinder

Bibliographic record

VenueIndian Journal of Endocrinology and Metabolism · 2017
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSikhismRelevance (law)MedicineChristianityPolitical sciencePhilosophyReligious studies

Abstract

fetched live from OpenAlex

Religion has been proposed as a means of enhancing patient and community acceptance of diabetes and cultural specific motivational strategies to improve diabetes care. Sikhism is a young and vibrant religion, spread across the world and the Holy Scripture Sri Guru Granth Sahib (SGGS) is regarded as the living Guru by all Sikhs. The three key pillars of Sikhism are Kirat Karni (honest living), Vand Chakna (sharing with others) and Naam Japna (focus on God). They can help encourage the diabetes care provider, patient and community to engage in lifestyle modification, shared responsibility, positive thinking and stress management. The verses (Sabads) from the SGGS, with their timeless relevance, span the entire spectrum of diabetes care, from primordial and primary, to secondary and tertiary prevention. They can provide us with guidance towards a holistic approach towards health and lifestyle related diseases as diabetes. The SGGS suggests that good actions are based on one's body and highlights the relevance of mind-body interactions and entraining the mind to cultivate healthy living habits. The ethics of sharing, community and inclusiveness all lay emphasis on the need for global and unified efforts to manage and reduce the burden of the diabetes pandemic.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.347
Teacher spread0.290 · 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 designOther design
Domainnot available
GenreReview

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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of Endocrinology and MetabolismSame topicDiabetes Management and EducationFrench-language works237,207