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Record W7005310115

Problematic Online Betting Among Turkish Adolescents

2019· article· en· W7005310115 on OpenAlexaboutno aff

Bibliographic record

VenueHasan Kalyoncu University Institutional Repository (Hasan Kalyoncu University) · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishThe InternetQuarter (Canadian coin)AddictionComputer-assisted web interviewing
DOInot available

Abstract

fetched live from OpenAlex

Problematic online betting among adolescents has attracted considerable public attention internationally for the last two decades. Although the online betting prevalence rate in Turkey is unclear, some reports indicate that it could be more pervasive than is currently estimated. The aim of this study was to determine the prevalence of problematic online betting, common behaviors of youth related to betting, and to identify the effect of family on online betting among Turkish adolescents. We surveyed 6116 adolescents aged between 12 and 18 in Istanbul to determine if they are problematic Internet users for betting. Although 756 (12.4%) adolescents reported that they play online betting, only 176 adolescents (2.9%) were classified as problematic Internet users. Thus, we collected further data from those 176 adolescents, 14.8% of which were female. A significant positive correlation was found between Internet Addiction (IA) and duration of betting. Almost 61% of participants expressed that they prefer to be online because they do not have better things to do. Almost a quarter of the participants started online betting between 10 and 12 years of age. All participants know someone who bets online. In terms of frequency, these are friends, relatives, siblings, and parents, respectively. Although there is no relationship between family structure and IA among adolescents who are problematic users, participants who live in an unstable family have higher IA scores than participants who live in a stable family

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.004
GPT teacher head0.184
Teacher spread0.179 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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