Sensory processing sensitivity, alexithymia, and eating disorder risk in adolescents in alternative care: A multi-informant network study
Bibliographic record
Abstract
Adolescents in alternative care show elevated eating disorder risk, yet mechanistic bridges between sensory reactivity and emotion-processing deficits remain underexplored. This study mapped the interplay among sensory processing sensitivity (SPS), alexithymia, and eating-related risk and tested moderation by trauma burden using a multi-informant design. Approximately 200 youths (12–18 years) were recruited from foster and residential services. Measures included self-report SPS (highly sensitive child facets), alexithymia (Toronto alexithymia scale-20: Difficulties identifying feelings [DIF], difficulties describing feelings [DDF], externally oriented thinking), eating-risk (eating attitudes test-26 subscales; binge eating scale), internalizing symptoms (patient health questionnaire for adolescents; screen for child anxiety related emotional disorders), and trauma (childhood trauma questionnaire–short form), along with covariates (age, sex, care type, body mass index z-score, pubertal status). Teachers/caregivers provided parallel SPS ratings. Primary analyses estimated a regularized partial-correlation network (Gaussian graphical model; EBICglasso) on domain-level nodes; accuracy and centrality stability were assessed through bootstrapping. Trauma moderation was evaluated through permutation-based network comparison tests; multi-informant integration was modeled by including teacher-SPS as a node and through informant-specific sensitivity analyses. SPS facets—especially Low Sensory Threshold and Ease of Excitation—bridged to alexithymia (DIF/DDF) and to eating-risk nodes, with stronger global connectivity observed under higher trauma exposure. Teacher-SPS converged with youth reports while adding unique variance. Limitations included the cross-sectional design, self-report bias, and setting selection. Findings delineate actionable psychosomatic targets—sensory-load management and affect labeling/interoceptive skills—for early, low-intensity interventions in foster/residential contexts.
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 | 0.002 | 0.005 |
| 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) | 0.002 | 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".