Identifying at-risk profiles and protective factors for problem gambling: A longitudinal study across adolescence and early adulthood.
Bibliographic record
Abstract
Past studies have identified various risk and protective factors for problem gambling (PG). However, no study has examined the interplay between these factors using a combination of person-centered and variable-centered approaches embedded within a longitudinal design. The present study aimed to (a) identify distinct profiles in early adolescence based on a set of risk factors commonly associated with PG (impulsivity, depression, anxiety, drug-alcohol use, aggressiveness, and antisociality), (b) explore the difference in reported gambling problems between these profiles during midadolescence and early adulthood, and (c) identify family- and peer-related variables that could operate as protective or compensatory factors in this context. Two samples were used: (a) a population sample (N = 1,033) living in low socioeconomic-status neighborhoods and (b) a population sample (N = 3,017) representative of students attending Quebec schools. Latent profile analyses were conducted to identify at-risk profiles based on individual risk factors measured at age 12 years. Negative binomial regression models were estimated to compare profiles in terms of their reported gambling problems at ages 16 and 23. Finally, family- and peer-related variables measured at age 14 were included to test their protective or compensatory role with respect to the link between at-risk profiles and gambling problems. Four profiles were identified: well-adjusted, internalizing, externalizing, and comorbid. Compared to the well-adjusted profile, the externalizing and comorbid profiles reported more gambling problems at ages 16 and 23, but the internalizing profile did not differ significantly. Various protective and compensatory factors emerged for each profile at both time points. (PsycINFO Database Record
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".