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Record W3126022374 · doi:10.4103/jfmpc.jfmpc_1354_20

Hazardous use of alcohol among men in the tribal population of Jawadhi Hills, Tamil Nadu: Nature, prevalence, and risk factors

2021· article· en· W3126022374 on OpenAlexaboutno aff
Anuradha Rose, VenkatRaghava Mohan, Amala Vinodh, Sam Marconi David, Kuryan George, Shantidani Minz, Jasmin Helan Prasad

Bibliographic record

VenueJournal of Family Medicine and Primary Care · 2021
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEnvironmental healthAlcoholPopulationQuarter (Canadian coin)Psychological interventionHazardous wasteDemographyGeographyPsychiatryWaste management

Abstract

fetched live from OpenAlex

BACKGROUND: Worldwide, hazardous use of alcohol is common among many cultures and societies and adversely impacts families and communities, with significant morbidity and mortality. Scheduled Tribes (STs) who are socially deprived and marginalised have higher rates of alcohol use. AIM: We attempted to determine the nature, prevalence, and risk factors associated with hazardous consumption of alcohol in the tribal community. METHODOLOGY: A cross-sectional study was conducted among adult male and permanent residents of Jawadhi hills. A total of 1200 men were interviewed. Study participants were chosen by Probability Proportionate to Size (PPS) sampling method. The questionnaire that documented socio-demographic characteristics and patterns of alcohol use was used. AUDIT tool was used to assess the hazardous use of Alcohol. Data were analysed using SPSS. RESULTS: Majority of the men were middle-aged, married, and were from lower socio-economic strata. A large proportion of men (65%) had a history of alcohol consumption in the last one year using one-year, of whom a quarter showed hazardous use (29%) and another quarter exhibited alcohol dependency (24%). Tobacco use, higher income and local alcohol production were found to be significant risk factors for Hazardous alcohol use. CONCLUSION: Alcohol consumption needs to be treated as a social problem and has to be tackled at the policy level. Population-based interventions, legislation, taxation, policies regarding the manufacture and sale of alcohol, are some of the ways to address this problem.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.301
Teacher spread0.259 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

Explore more

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