ICSPConference Proceedings 2022
Bibliographic record
Abstract
Complex posttraumatic stress disorder (cPTSD) has several detrimental consequences, including severe anxiety, emotional detachment, mood irregularities, and vivid flashbacks to trauma.In many cases, cPTSD can be linked back to adverse childhood experiences (ACEs).Treatments for cPTSD that involve psychedelic drugs are potentially beneficial, but unfortunately they are understudied in psychology labs due to their classification as a Schedule I substance.Thus, theoretical work is needed to explain potential mechanisms involved in treatment programs.In this new theoretical model, I clarify the mechanistic links between ACEs and cPTSD and then examine why psychedelic drugs may be an ideal therapeutic tool for the treatment of cPTSD.Toxic stress theory posits that exposure to extreme, frequent, and persistent ACEs without the presence of a supportive caretaker chronically activates the stress response system (Jones et al., 2021).Toxic stress results in dysregulation of the limbic-hypothalamic-pituitary-adrenal (LHPA) axis, elevating levels of catecholamines, cortisol, and proinflammatory cytokines (Thermo Fisher Scientific, n.d.).The toxic stress induced by ACEs causes cPTSD due to the persistent exposure to multiple adverse events leading to re-experience of the traumatic events, avoidance behaviors, and paranoia.Psychedelic drugs unlock repressed memories, engaging positively with negative self-concept and dysregulated emotions, which are both characteristic of the Disturbances of Self-Organization symptom cluster of cPTSD.Presentation of this theoretical model would allow for public recognition of the potential benefits of this treatment and further exploration into this topic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.697 | 0.595 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".