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Record W2118577709 · doi:10.5539/gjhs.v5n6p40

A Comparative Study of Self-Regulation in Substance Dependent and Non-Dependent Individuals

2013· article· en· W2118577709 on OpenAlexvenueno aff
Nour‐Mohammad Bakhshani, Mohsen Hosseinbor

Bibliographic record

VenueGlobal Journal of Health Science · 2013
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingSubstance dependenceSubstance usePsychologyClinical psychologyChecklistDescriptive statisticsAssertivenessSubstance abuseSelf-controlPsychiatryDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Several factors influence the beginning and maintenance of substance use. The purpose of this study was to examine as well as to compare 'self-regulation' in both substance dependent and non-substance dependent individuals. METHOD: In a cross-sectional study 228 (118 substance dependent and 110 with no history of using substance) participants aged 16-55 were recruited. All of the participants were asked to complete the Self-Regulation Inventory (SRI-25) and a demographic characteristics data checklist. Data was analyzed using descriptive statistics (frequency, mean and standard deviation) and the t-test. RESULTS: The results showed significant differences between substance dependent and non- substance dependent groups in all the scales of the self-regulation inventory including positive actions, controllability, expression of feelings and needs, assertiveness, and well-being seeking (p<0.01). CONCLUSION: Self-regulation and self-control skills in drug dependent individuals are lower than those without substance dependence individuals. It is concluded that substance use may related to a deficiency in self-control and regulation of feelings. Therefore, for prevention and treatment of substance dependence disorder, it is necessary to work out and exploit strategies that include the improvement of self-regulation.

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)0.002
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 score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.073
GPT teacher head0.441
Teacher spread0.368 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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