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Record W4392116722 · doi:10.15173/m.v1i42.3275

Sarab Rog Ka Aukhad Naam

2022· article· en· W4392116722 on OpenAlexvenueaboutno aff
Jasmine Uppal

Bibliographic record

VenueThe Meducator · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

“Sarab rog ka aukhad naam” (“The recitation of God’s name cures all diseases”) is a quotation from the Guru Granth Sahib —the Sikh Holy Book— highlighting Sikhism’s perspective on illness, referring to the Guru Granth Sahib as the primary source of guidance regarding issues of health. However, in the decades since this quotation was first written, the burden of chronic illness in the Sikh community has become more severe. Notably, cardiovascular disease (CVD) has become increasingly prevalent within the Sikh and broader South Asian (SA) populations. Several studies have outlined how the SA community has the highest prevalence of CVD in Canada, along with a higher CVD mortality rate compared to other ethnic groups. Although some biological factors may explain the SA community’s increased risk for CVD, there is no denying the social and psychological factors at play. It has been established that sedentary behaviour, depression, and psychological stress increase CVD risk. As such, the fact that SA individuals in Canada are more likely to be sedentary and diagnosed with depression than their non-SA counterparts only intensifies their likelihood of developing CVD. Moreover, much of the SA population in the West exists as visible racial and religious minorities, subjecting them to additional social and psychological harms, including stress stemming from migration and discrimination, all of which contribute to CVD. SA individuals in Canada are also more likely to experience lower quality-of-life one year post-surgical interventions for CVD-related events, as well as increased recurrent CVD events. That is, not only are SAs more likely to develop CVD, but their disease prognosis appears to be worse than non-SAs. As such, improving CVD management procedures for the SA population would prove valuable, given the significant prevalence of the disease in this community. More specifically, areas with a prominent SA population, like the Peel Region of Ontario, should consider providing CVD interventions specific to SAs, such as culturally sensitive rehabilitation programs. As SA is a broad term encompassing many distinct ethnic and cultural backgrounds, this paper recommends focusing on interventions tailored to a specific population within the SA community, to avoid cultural generalizations. For the purposes of this paper, the focus population is the Sikh community, defined specifically as individuals with an ethnic background from the Punjab region in India who practice Sikhism. The Sikh community as a target population is optimal as it falls under the broader SA community and it is one of the largest religious minorities in the Peel Region; thus, this plan would ensure that a sizeable community in the Peel Region receives access to treatment that acknowledges their religious and cultural diversity.10 Peel Regional Health should design a rehabilitation plan specific to the Sikh community for patients diagnosed with CVD, or recovering from a CVD-related incident. However, it is critical to note that there are certain challenges associated with implementing such a program, specifically relating to funding issues and the program’s perceived practicality.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.070
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0700.024

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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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