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Record W2064847926 · doi:10.3109/14659891.2012.663452

How do drug and alcohol use relate to parental bonding and risk perception in university students?

2012· article· en· W2064847926 on OpenAlexafffundabout
Tomas Jurcik, Richard Moulding, Emma Naujokaitis

Bibliographic record

VenueJournal of Substance Use · 2012
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcGill UniversityConcordia University
FundersNational Institute on Drug AbuseMcGill University
KeywordsPerceptionPsychologyCannabisResidenceIllicit drugSubstance useRisk perceptionAlcoholDevelopmental psychologyDrugEnvironmental healthClinical psychologyMedicineDemographyPsychiatry

Abstract

fetched live from OpenAlex

Alcohol and drug use are major health concerns on university and college campuses. It has previously been found that parental rearing patterns are related to the frequency of substance use. Further, perceptions that drug use is dangerous have been found to be related to less substance use. However, little research has directly examined the impact of parental rearing patterns on substance use by university students, and no research has examined the effects of both risk perception and parenting on substance use. Therefore, this research surveyed the frequency and extent of alcohol, cigarette and illicit drug use by students (N = 336) at a Canadian university residence, classes and health services and examined the relationship between the results with parental bonding and risk perception. It was found that “affectionless control” parenting patterns in the mother, but not the father, were related to greater drinking and drinking problems and to the use of illicit substances. Lower perceptions of risk were related to greater use of alcohol, cigarettes, cannabis and other illicit substances. Unexpectedly, there was little relationship between parental rearing and risk perceptions, suggesting that there are other avenues whereby parenting leads to greater alcohol use. Implications are discussed.

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 score0.006
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.034
GPT teacher head0.281
Teacher spread0.247 · 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

Citations12
Published2012
Admission routes3
Has abstractyes

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