A model exploring the relationship between betrayal trauma and health: The roles of mental health, attachment, trust in healthcare systems, and nonadherence to treatment.
Bibliographic record
Abstract
OBJECTIVE: Prior research suggests that there is a relationship between traumatic experiences and poor health. When considered through the lens of betrayal trauma (i.e., the perpetrator and the victim have a close interpersonal relationship), traumatic experiences predict greater posttraumatic difficulty and higher levels of depression. Betrayal trauma has been associated with poorer interpersonal relationships and less trust in individuals and systems that may be important for a person's wellbeing, such as health care systems. In turn, trauma survivors are less likely to adhere to medical treatment, which may ultimately affect their overall health. The current study examined the complex relationship between experiences of betrayal trauma and poor health, while accounting for demographics, mental health symptoms, trust in physicians and the medical system, attachment style, and nonadherence to medical treatment. METHOD: A demographically representative sample of 312 Canadian participants was surveyed online. Participants completed measures that assessed symptoms of mental health (PTSD, depression), trauma, attachment style, trust, and nonadherence to medical treatment. RESULTS: Hierarchical regression models were used to examine the relationship between betrayal trauma and health. Betrayal trauma significantly predicted nonadherence to treatment, while trust in physicians was explained by trauma, attachment style, and mental health symptoms. All of these factors significantly explained poor health status. CONCLUSIONS: Results suggest the importance of implementing trauma-informed care in health care systems. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".