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Record W2119999713 · doi:10.5539/ass.v10n8p165

Hardiness and Problem-Solving Skills as Preventive Factors against Smoking among Adolescents

2014· article· en· W2119999713 on OpenAlexvenueno aff
Abbas Abdollahi, Mansor Abu Talib

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsHardiness (plants)PsychologyStructural equation modelingCigarette smokingCoping (psychology)Locus of controlSelf-efficacyClinical psychologyDevelopmental psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Smoking is the biggest threat to public health, and it remains a serious cause of death in the world; therefore, it is essential to develop our knowledge concerning the attitudes among adolescents toward cigarette smoking. This study was conducted to examine the associations among hardiness, problem-solving skills appraisal and attitude towards cigarette smoking in adolescents. The participants comprised of 550 high school students from Tehran, completed the self-report questionnaires. The structural equation modelling (SEM) estimated that adolescents with a high ability for hardiness, effective problem-solving confidence, internal personal control and approaching coping styles were more likely to report negative attitude towards cigarette smoking. The findings promote the value of problem-solving skills and hardiness as protective factors against smoking.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.339
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2014
Admission routes1
Has abstractyes

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