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Record W3194303063 · doi:10.3968/12193

The Level of Prevalence of Irrational Beliefs among Drug Addicts in Jordan

2021· article· en· W3194303063 on OpenAlexvenueno aff
Yacoub Fareed Al Farah

Bibliographic record

VenueHigher education of social science · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Treatments and Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsIrrational numberFeelingAddictionAnxietyPsychologyDrug addictScale (ratio)Clinical psychologySample (material)Social psychologyPsychiatryGeographyMathematics

Abstract

fetched live from OpenAlex

The study aimed to discover the prevalence of irrational beliefs among drug addicts in Jordan and find out the difference in the level of prevalence of these beliefs among them according to their educational qualifications and ages. And to achieve this, the descriptive survey approach was used, and the questionnaire was used as a tool to collect data. The study sample consisted of (103) addicts in Amman, who were chosen using the available sample method, and the data were collected using the irrational beliefs scale prepared by Ellis and translated (Al-Rihani, 1985). The results showed the prevalence of irrational beliefs among drug addicts in Jordan, at high levels in general, and a mean of (2.36). The results also showed that there are statistically significant differences at the level of (α ≤ 0.05) on the scale of irrational beliefs and the areas (Cruel blaming of oneself and others, excessive anxiety, dependence, feeling helpless, and the importance of past experiences) attributed to the educational qualification variable. In contrast, there were no statistically significant differences on the scale of irrational beliefs and in all domains due to the age variable. The study recommended focusing on cognitive rebuilding in drug addicts to modify their irrational convictions and prepare treatment programs based on the theory of rational, emotional therapy to help specialists identify irrational beliefs and work to remove them in addicts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.423
Teacher spread0.338 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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