Press Play to Feel: The Role of Attachment Styles and Alexithymic Features in Problematic Gaming
Bibliographic record
Abstract
Problematic gaming has been consistently associated with insecure attachment styles and alexithymia. However, there is limited knowledge regarding the impact of specific alexithymic features and insecure attachment styles on problematic gaming. The study included a sample of 358 online game players (242 males, 67.6%) between the ages of 18 and 59 (M = 28.46; SD = 8.76) who were recruited from online gaming communities. The participants completed a sociodemographic schedule and measures on attachment styles, alexithymia, and problematic gaming. The results provide evidence for a positive prediction of problematic gaming by dismissing attachment style and the alexithymic factors concerning the difficulty identifying feelings and externally oriented thinking, even when controlling for potentially confounding factors, such as age, sex, education, marital status, and self-reported time devoted to online games. Additionally, the analysis revealed a significant interaction effect between externally oriented thinking and dismissing attachment style in the prediction of problematic gaming. The interaction implies that the alexithymia factors pertaining to externally oriented thinking hold significant relevance in predicting problematic gaming behaviors, especially in cases where dismissive attachment levels are moderately to highly present. These findings emphasize the significance of considering specific insecure attachment styles and alexithymic features when studying problematic gaming behaviors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".