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Record W2144589748 · doi:10.3126/jpan.v3i2.12379

Cannabis, Lord Shiva and Holy Men: Cannabis Use Among Sadhus in Nepal

2015· article· en· W2144589748 on OpenAlexaboutno aff
Sujata Acharya, John Howard, Samrat Panta, S S Mahatma, Jan Copeland

Bibliographic record

VenueJournal of Psychiatrists Association of Nepal · 2015
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisHonourTraditional medicineMedicineHinduismPsychiatryQuarter (Canadian coin)HarmEnvironmental healthGeographyPsychologyReligious studiesSocial psychology

Abstract

fetched live from OpenAlex

Background: Despite being illegal in Nepal, cannabis grows wild, is cultivated, readily available and often consumed during religious festivals, such as those in honour of the Hindu god Shiva. Holy men (sadhus) also consume cannabis to aid meditation, and many are believed to suggest that as a substance favoured by Lord Shiva, and, as such, should be used. However, there are concerns that all cannabis use in Nepal is not benign, and that there are negative health and social consequences from its use for some consumers. Objectives: This study sought the views of sadhus in Nepal.Method: During the major Shiva festival at Pashupathinath temple complex in Kathmandu, Nepal, 200 sadhus were surveyed. Results: Most used cannabis daily, a quarter believed cannabis and its use to be legal in Nepal, and a further ten percent were unsure, about one third believed cannabis should be used by Hindus, but only fourteen believed Lord Shiva promoted its use. Those less educated and from the Naga sect were more likely to hold such views, and provide cannabis to devotees. Conclusions: Sadhus with evidence-based information about cannabis and its potential harms can play an important role in assisting to reduce harm and facilitate engagement in treatment. J Psychiatric Association of Nepal Vol .3, No.2, 2014, pp:9-14DOI: http://dx.doi.org/10.3126/jpan.v3i2.12379

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.304
Teacher spread0.285 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

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