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Record W4386835811 · doi:10.3390/bs13090778

Exploring Substance Abuse and the Dark Tetrad in Health Sciences and Non-Health Sciences Students

2023· article· en· W4386835811 on OpenAlexaff
Marina Carvalho de Moraes, Giulia Cunha Russo, Julia da Silva Prado, Ariela Raissa Lima‐Costa, Bruno Bonfá-Araújo, Julie Aitken Schermer

Bibliographic record

VenueBehavioral Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychopathyNarcissismMachiavellianismPsychologyPersonalityTetradBig Five personality traitsClinical psychologyDark triadSubstance abusePopulationDevelopmental psychologyPsychiatrySocial psychologyMedicineEnvironmental healthBiology

Abstract

fetched live from OpenAlex

Substance abuse can be used as a coping strategy to manage stress related to academic activities and is a risk-taking behavior that is also associated with people with higher levels of the Dark Tetrad personality traits. Our study aimed to investigate the association between substance abuse and the Dark Tetrad in students in health and non-health sciences fields. Our sample was composed of 174 college students between 18 and 58 years old (M = 25.60; SD = 9.14). Students completed self-report psychopathy, narcissism, Machiavellianism, sadism, and substance use scales. Results suggest that men consumed more substances and scored higher on the Dark Tetrad than women. Also, when comparing fields, men from health sciences tended to score higher on dark personality traits. These results emphasize the potential risk factors associated with dark personality traits and the consumption of licit and illicit substances by college students, highlighting the need for further studies with this population and the impact of these behaviors and characteristics on future professional practice.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.006
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.393
GPT teacher head0.499
Teacher spread0.106 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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