Posttraumatic Stress Disorder Part III: Health Effects of Interpersonal Violence Among Women
Bibliographic record
Abstract
TOPIC: The aim of this three-part series is to examine the sufficiency of the posttraumatic stress (PTSD) diagnostic construct to capture the full spectrum of human responses to psychological trauma. Part I (Lasiuk & Hegadoren, 2006a) reviewed the conceptual history of PTSD from the nineteenth century to its inclusion in the third edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1980), while Part II (Lasiuk & Hegadoren, 2006b) described subsequent refinements to the original PTSD diagnostic criteria and highlighted subsequent controversies. PURPOSE: This paper focuses on interpersonal violence (sexual, physical, and emotional abuse/assault) and its sequelae in women. We argue in support of Judith Herman's (1992) conceptualization of the human trauma response as a spectrum, anchored at one end by an acute stress reaction that resolves on its own without treatment, and on the other by "complex" PTSD, with "classic" or "simple" PTSD somewhere between the two. SOURCES OF INFORMATION: he existing theoretical, clinical and research literatures related to humans responses to trauma. CONCLUSION: The paper concludes with a call for the need to increase a gendered perspective in all aspects of trauma research and clinical service delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".