Alexithymia as a Risk Factor for an Internet Addiction in Adolescents and Young Adults with Autism Spectrum Disorder
Bibliographic record
Abstract
The aim of the study is to investigate the association of alexithymia with Internet addiction and autism spectrum disorders among adolescents and young adults. The links between alexithymia, ASD and other mental disorders are still a largely unexplored topic in psychiatry. An intriguing question is to what extent alexithymia can be a component of the clinical picture of ASD, and to what extent it is an independent phenomenon often co-occurring with ASD. The study group consisted of young Poles aged 11 to 35 (n = 229), including women (n = 167; 73%), men (n = 53; 23%) and non-binary people (n = 9; 4%). The following questionnaires were used in the Polish validated version as screening tools and shared online: AQ (Autism Quotient), TAS-20 (Toronto Alexithymia Scale), IAT (Internet Addiction Test). Among the subjects, 15 people admitted that they had received an official diagnosis of ASD, while 26 people showed a significantly increased severity of autistic traits on the AQ questionnaire. People with ASD who also exhibit alexithymia features are certainly more prone to problematic use of the Internet. In contrast, such a risk in people with ASD without alexithymia is comparable to the general population.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".