Alexithymia in Adult Autism Clinic Service-Users: Relationships with Sensory Processing Differences and Mental Health
Bibliographic record
Abstract
Mental health difficulties commonly co-occur with autism, especially in autistic people accessing clinic services, impacting substantially on quality-of-life. Alexithymia (difficulty describing/identifying feelings) and sensory processing differences are prevalent traits in autism that have been associated with depression/anxiety in autistic community samples. However, it is important to better understand interrelationships between these traits in clinical populations to improve identification of service-user needs. In this study, 190 autistic adults (65.3% male), seen in a tertiary autism clinic, completed self-report measures of alexithymia (20-item Toronto Alexithymia Scale), sensory processing differences (Adolescent/Adult Sensory Profile) and depression/anxiety (Hospital Anxiety and Depression Scale). Multiple linear regression models and mediation analyses were used to examine associations between alexithymia, sensory processing differences, and depression/anxiety severity. Across the sample, 66.3% of individuals (N = 126) were classified as alexithymic (score ≥ 61). Total alexithymia and difficulty describing/identifying feelings were significantly associated with depression severity (β = 0.30–0.38, highest p < 0.002), and difficulty identifying feelings was significantly associated with anxiety severity (β = 0.36, p < 0.001). Sensory processing differences were also significantly associated with depression severity (β = 0.29, p = 0.002) and anxiety severity across all models (β = 0.34–0.48, highest p < 0.001) Finally, difficulty describing/identifying feelings partially mediated the relationships between sensory processing differences and both depression/anxiety severity. Overall, these results highlight that interventions adapted for and targeting emotional awareness and sensory-related uncertainty may improve mental health outcomes in autistic service-users.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".