Prevalence and associated factors of alexithymia among adult prisoners in China: a cross-sectional study
Bibliographic record
Abstract
Abstract Background Prison is an extremely stressful environment and prisoners have an increasing risk of suffering from alexithymia. Therefore, this study aims to investigate the prevalence and associated factors of alexithymia among prisoners in China. Methods A cross-sectional study was conducted in five main jails of the district of Zhejiang province in China, and a total of 1705 adult prisoners ultimately took part in the study. Toronto Alexithymia Scale, Childhood Trauma Questionnaire, Beck Depression Inventory, Beck Anxiety Inventory, Beck Hopelessness Scale and several short demographic questions were applied. Results Over 30% of prisoners were classified as alexithymics and as high as 96.2% of prisoners suffered from at least one traumatic experience in their childhood, meanwhile, 81.5%, 53.4% and 85.8% were found to be positive for depression, anxiety and hopelessness symptoms respectively. Education, childhood trauma, negative emotional symptoms including depression, anxiety and hopelessness of the respondents, were negatively or positively associated with alexithymia among prisoners. Conclusions The results indicated that high prevalence of alexithymia among prisoners is linked with their level of education, experience of childhood trauma and symptoms of negative emotions. Accordingly, the findings in our study can be used for prevention and intervention of alexithymia among prisoners.
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 | 0.001 | 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) | 0.001 | 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".