Positive Symptoms of Psychosis and International Classification of Diseases 11th Revision (ICD-11) Complex Post-traumatic Stress Disorder: A Network Analysis in a Canadian Sample from Montreal: Symptômes positifs de psychose et trouble de stress post-traumatique complexe (CIM-11): Une analyse de réseau dans un échantillon canadien de Montréal
Bibliographic record
Abstract
Objectives Traumatic experiences constitute a risk factor for developing different psychopathologies, such as post-traumatic stress disorder (PTSD), complex PTSD (CPTSD), and positive symptoms of psychosis. However, on the symptom level, it is still unclear how CPTSD and positive symptoms of psychosis associate with each other. The present study aimed to shed light on these dynamics by investigating the symptoms network of CPTSD and positive symptoms of psychosis. Methods A network analysis was performed on CPTSD and psychosis symptoms among a Canadian community sample with a history of traumatic life events ( n = 747). Measures included the international trauma questionnaire and the mPRIME screen. Results In total, 4.8% of the sample reached the criteria of probable PTSD and 7% fulfilled the criteria of probable CPTSD. PTSD and CPTSD groups had a significantly higher severity of positive symptoms of psychosis compared to the no-disorder group. Network analysis revealed 3 distinct communities of symptoms of PTSD, disturbances in self-organization, and psychosis. Affective dysregulation served as the bridging symptom between the communities. Hearing one's own thoughts aloud was the most central symptom in the network. Conclusions Findings show that positive symptoms of psychosis can be considered trauma-related responses. Furthermore, interventions targeting affective dysregulation as well as the experience and distress associated with hearing one's own thoughts aloud may contribute to symptom reduction and improved functioning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".