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Record W1989403461 · doi:10.5539/gjhs.v6n6p43

Intention and Willingness in Understanding Ritalin Misuse Among Iranian Medical College Students: A Cross-Sectional Study

2014· article· en· W1989403461 on OpenAlexvenueno aff
Ahmad Ali Eslami, Farzad Jalilian, Mari Ataee, Mehdi Mirzaei-Alavijeh, Mohammad Mahboubi, Afsar Ali, Abbas Aghaei

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsnot available
FundersIsfahan University of Medical Sciences
KeywordsCross-sectional studyPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

Ritalin misuse can create powerful stimulant effects and serious health risks. The main aim of present study was compared that two cognitive construct (behavioral intention or behavioral willingness) for predicting Ritalin misuse. This cross-sectional study was conducted among 264 Iranian medical college students; participants selected in random sampling, and data were collected by using self-report questionnaire. Data were analyzed by SPSS version 21 at 95% significant level. Our findings showed, the three predictor variables of (1) attitude, (2) subjective norms, and (3) prototype accounted for 29% of the variation in intention and 25% of the variation in willingness to Ritalin misuse. In addition, behavioral intention was a stronger prediction factor compared to willingness for Ritalin misuse, with odds ratio estimate of 1.607 [95% CI: 1.167, 2.213]. There is some support to use the prototype willingness model to design interventions to improve individuals' beliefs that academic goals are achievable without the misuse of Ritalin.

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)0.010
metaresearch head score (Gemma)0.004
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 score0.010
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.128
GPT teacher head0.448
Teacher spread0.320 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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