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Record W2342209533 · doi:10.4082/kjfm.2015.36.5.210

Perceived Stress, Alexithymia, and Psychological Health as Predictors of Sedative Abuse

2015· article· en· W2342209533 on OpenAlexaboutno aff
Nader Rajabi Gilan, Ali Zakiei, Sohyla Reshadat, Saeid Komasi, Seyed Ramin Ghasemi

Bibliographic record

VenueKorean Journal of Family Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaSedativeMedicineClinical psychologyAnxietySubstance abusePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The harmful effects of sedative medications and substances in conjunction with limited research regarding predictive psychological constructs of drug abuse necessitate further investigation of associated factors. Therefore, the present study aimed to elucidate the roles of perceived stress, alexithymia, and psychological health as predictors of sedative abuse in medical students. METHODS: In this cross-sectional study, 548 students at Kermanshah University of Medical Sciences, Iran, were selected using stratified random sampling. The data were obtained using the Perceived Stress Scale, an alexithymia scale (Farsi version of the Toronto Alexithymia Scale-20), and a General Health Questionnaire to assess psychological health. Data were analyzed using discriminant analyses. RESULTS: The results demonstrated that the user and non-user of sedative substances groups had significantly different predictive variables (except for social function disorder) (P>0.05). Physical complaints, alexithymia, and perceived stress, which had standard coefficients of 0.80, 0.60, and -0.27, respectively, predicted sedative drug use. CONCLUSION: The results of the present study indicate that perceived stress, alexithymia, physical complaints, anxiety, and depression are associated with sedative drug abuse.

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)0.001
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 score0.097
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.090
GPT teacher head0.370
Teacher spread0.279 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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