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Record W2058069233 · doi:10.6000/1929-4409.2015.04.07

Consequences of Drug Abuse among Female and Male Population of Karachi: A Statistical Surveyed Approach

2015· article· en· W2058069233 on OpenAlexvenueno aff
Rana Saba Sultan, Jawed Aziz Masudi, Afaq Ahmed Siddiqui, Najia Mansoor

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubstance abuseAffect (linguistics)PsychiatryDrugPopulationEnvironmental healthDemographyDrug abuserGerontologyPsychology

Abstract

fetched live from OpenAlex

Drugs are chemicals. Different drugs, because of their chemical structures, can affect the body in different ways. The most obvious effects of drug abuse which are manifested in the individuals include ill health, sickness and ultimately, death. The social life is also not spared by the hazardous impacts of the problem. Whereas the load at health department is increased, rise in crime rate is also a perilous effect faced by the society related to the growth of abusers in the country. The following study highlights the different effects that can influence male and female drug abusers to get rid of their drug misuse habits. Abusers age, level of awareness about drugs adverse effects, their encounters to health ailments including the life threatening infection HIV, and involvement in crimes were included in the survey which was carried out in Karachi in order to assess the magnitude of 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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.138
GPT teacher head0.393
Teacher spread0.254 · 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
Published2015
Admission routes1
Has abstractyes

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