Problematic Online Betting Among Turkish Adolescents
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".