Developing an intense player-avatar relationship and feeling disconnected by the physical body: A pathway towards internet gaming disorder for people reporting empty feelings?
Bibliographic record
Abstract
Abstract The literature suggests that alexithymia and emptiness could be risk factors for various addictive behaviors. The present study developed and tested a model that proposes a pathway leading from emptiness and difficulties in identifying emotions to Internet Gaming Disorder (IGD) symptoms via an intense gamer-avatar relationship and bodily dissociative experiences. A sample of 285 (64.2% M; mean age = 30.38 ± 7.53) online gamers using avatar-based videogames was recruited from gaming communities, and they were asked to complete a survey that included the Toronto Alexithymia Scale, the Subjective Emptiness scale, the Scale of Body Connection, the Self-Presence Questionnaire, and the Internet Gaming Disorder Scale-Short Form. The structural model evaluated produced a good fit to the data [χ2 = 175.14, df = 55, p < .001; RMSEA = 0.08 (90% C.I. =0.07–0.09), CFI = 0.96, SRMR = 0.08] explaining 28% of the total variance. Alexithymia was indirectly associated with IGD through the serial mediation of the gamer-avatar relationship and body dissociation. Emptiness was associated with IGD symptoms at the bivariate level, but did not predict IGD directly or indirectly. The current study identifies a potential pathway toward IGD by integrating different lines of research, showing the importance of considering aspects such as the difficulty in recognising and expressing one’s emotions, the gamer- avatar relationship, and the mind-body connection in the context of IGD.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".