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Spirituality, gender, and drug use among students from one University in Barbados

2015· article· en· W2376032046 on OpenAlexafffund
M Jules, Samuel Noh, Hayley A. Hamilton, Bruna Brands, Denise Gastaldo, Maria da Glória Miotto Wright, Francisco Cumsille, Akwatu Khenti

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsCentre for Global Health ResearchPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersForeign Affairs and International Trade CanadaGovernment of CanadaCentre for Addiction and Mental Health
KeywordsSpiritualityLatin AmericansContext (archaeology)PsychologyMedicineClinical psychologyAlternative medicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

The research aimed to clarify the relationships between spirituality, gender, and drug use among undergraduate students of one university in Barbados. A quota sample of 250 students was required from the Faculty of Social Sciences as the study was part of a larger multi-centric study involving students from the Faculty of Social Sciences from seven other participating countries in Latin America and the Caribbean. Significant negative relationships were found between a student's level of spirituality and their licit and illicit drug use over the past 12 and three months. Males and females also differed significantly in terms of illicit drug use (past 12 and three months). However no gender differences were found as it related to licit drug use. Moreover, females possessed significantly higher levels of spirituality than their male counterparts. Further qualitative research should be conducted among this age group within the Barbadian context to better explain these findings.

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 score0.172
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.503
GPT teacher head0.484
Teacher spread0.019 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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