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Record W2481661685 · doi:10.1080/02791072.2016.1211352

Substance Use among a Sample of Healthcare Workers in Kenya: A Cross-Sectional Study

2016· article· en· W2481661685 on OpenAlexaff
Aggrey Gisiora Mokaya, Victoria Mutiso, Abednego Musau, Albert Tele, Yeri Kombe, Zipporah Ng’ang’a, Erica Frank, David M. Ndetei, Veronic Clair

Bibliographic record

VenueJournal of Psychoactive Drugs · 2016
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsCannabisMedicineHealth careKenyaCross-sectional studyEnvironmental healthAddictionPsychiatrySubstance usePopulationSubstance abuse

Abstract

fetched live from OpenAlex

This study describes reported substance use among Kenyan healthcare workers (HCWs), as it has implications for HCWs' health, productivity, and their ability and likelihood to intervene on substance use. The Alcohol Smoking and Substance Involvement Screening Test (ASSIST) was administered to a convenience sample of HCWs (n = 206) in 15 health facilities. Reported lifetime use was 35.8% for alcohol, 23.5% for tobacco, 9.3% for cannabis, 9.3% for sedatives, 8.8% for cocaine, 6.4% for amphetamine-like stimulants, 5.4% for hallucinogens, 3.4% for inhalants, and 3.9% for opioids. Tobacco and alcohol were also the two most commonly used substances in the previous three months. Male gender and other substance use were key predictors of both lifetime and previous three months' use rates. HCWs' substance use rates appear generally higher than those seen in the general population in Kenya, though lower than those reported among many HCWs globally. This pattern of use has implications for both HCWs and their clients.

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 score0.007
Threshold uncertainty score0.406

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 communication0.0000.001
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 head0.047
GPT teacher head0.359
Teacher spread0.312 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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