On Depersonalization Disorder: State Decentering and State Dissociation 
Bibliographic record
Abstract
This study aimed to look at the correlations between depersonalization, mindfulness, and specifically the decentering aspect of mindfulness. This study is a correlational design, where 144 participants completed the Mindfulness Attention Awareness Scale (MAAS), Cambridge Depersonalization Scale (CDS), the Clinician Administered Dissociative State Scale (CADSS), and the Toronto Mindfulness Scale’s subscale for state decentering (TMS-D) on an online Qualtrics survey. It was predicted those higher in depersonalization would also be higher in state decentering. A Pearson’s r was conducted. In line with the hypothesis, both trait, and state depersonalization positively correlated with state decentering. Results also replicate the overall negative relationship between mindfulness and depersonalization. This implies mindfulness is multi-faceted, with many positives for those that experience depersonalization; however, a focus on decentering may not be the best course of treatment. Future studies should continue to examine the effects of mindfulness-based interventions (MBIs) on depersonalization and the effects excessive decentering could have.
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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".