The Role of Alexithymia Traits in Compulsive Skin Picking Behaviors
Bibliographic record
Abstract
Compulsive skin picking (SP) consists of repetitive picking behaviors resulting in severe tissue damage associated with marked distress. It is a psychiatric condition which has been recently included in the “obsessive compulsive and related disorders” chapter of the DSM-5 (American Psychiatric Association, 2013). Recent research identified an “automatic” subtype of SP behaviors that occurs outside of one’s awareness, including situations in which the individual picks his/her skin while engaged in a sedentary activity, such as watching television, and a “focused” subtype, a more intentional behavior engaged in response to negative emotions (Walther, Flessner, Conelea, & Woods, 2009). According to the Emotion Dysregulation Theory, (Roberts, O'Connor, & Bélanger, 2013), compulsive SP behaviors could be a coping strategy to face negative feelings. Thus, clinical characteristics such as impulsivity and emotion dysregulation have been demonstrated to be linked with SP behaviors in several previous studies. However, current research did not investigate the role of alexithymia, which could suggest the need for different therapeutic options for SP subtypes. Alexithymia refers to a group of cognitive and affective personality traits, including difficulties in recognizing and verbalizing feelings, paucity of fantasy life, concrete speech and thought closely linked to external events. The current study examined the role of alexithymia traits in SP behaviors controlling for depression and impulsivity in a large community sample. Four hundred twenty-five participants from the general population completed the Milwaukee Inventory for the Dimensions of Adult Skin Picking (MIDAS), the Beck Depression Inventory-II (BDI-II), Barratt Impulsiveness Scale-11 (BIS-11) and the Toronto Alexithymia Scale-20 (TAS-20). The results showed that higher scores on BIS-11 (β= .27, p<.01) and BDI-II (β= .18, p<.01) predicted higher scores on MIDAS Automatic. Higher scores on BDI-II (β= .20, p<.01), BIS-11 (β= .18, p<.01), and TAS-20 Difficulty Identifying Feelings (β= .13, p<.01) predicted higher scores on MIDAS Focused. Higher scores on BDI-II (β= .21, p<.01) and BIS-11 (β= .20, p<.01) predicted higher scores on MIDAS Mixed. Overall, more intense alexithymia traits were associated only with stronger focused picking behaviors and not with automatic and mixed behaviors. These findings partially supported the Emotion Dysregulation Theory since SP behaviors could be a coping strategy adopted to manage negative feelings by individuals with more intense alexithymia and impulsivity traits. Difficulty Identifying Feelings could be an alexithymic trait involved in focused SP behaviors. Knowledge of the link between alexithymia and this subtype of SP behaviors could suggest the use of treatment strategies specialized to target emotions awareness, recognition and definition. Limitations of the study, such as the correlational design and the lack of a clinical group are discussed to highlight research directions. Implications of the results for clinical practice are addressed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".