The Health Impact of Childhood Trauma: An Interdisciplinary Review, 1997-2003
Bibliographic record
Abstract
Research has shown a strong relationship between childhood trauma and psychological difficulties in later life; more recent research has indicated that the long-term effects are even greater for physical illness. These long-term effects have highlighted Posttraumatic Stress Disorder (PTSD) as a possible mediator variable. The illnesses identified include, but are not limited to, eating disorders, substance abuse, phobias, multiple personality disorders, irritable bowel syndrome, rheumatoid arthritis, and autoimmune disorders. Childhood trauma is an area of interdisciplinary interest; therefore, a variety of disciplines have been carrying out research in this area. This article is an integrative review of the literature over the last five years across disciplines, including nursing, medicine, psychology, education, social services, and government agencies. The review searched for themes, common constructs, and definitions, plus gaps in the present literature that need to be addressed. Particular attention was paid to measurement tools, and the importance of post traumatic stress disorder. Databases included were Medline, Proquest, Canadian Health Network (CHN), Canadian Business and Current Affairs (CBCA) Education, Educational Resources Information Center (ERIC) Plus, Cumulative Index to Nursing & Allied Health Literature (CINAHL), American Psychological Association Database Information (PsychINFO), and Cochrance.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".