Primary care experiences of women with a history of childhood trauma and chronic disease: Trauma-informed care approach.
Bibliographic record
Abstract
OBJECTIVE: To understand the primary care experiences of women who have a history of childhood trauma and chronic disease. DESIGN: Qualitative study using in-depth interviews with directed content analysis. SETTING: Family health team in Kingston, Ont. PARTICIPANTS: Twenty-six women. METHODS: Letters of invitation were sent to eligible participants followed by a telephone survey. Women with an adverse childhood experience (ACE) score of 4 or higher and with 2 or more chronic conditions were invited to participate in a one-on-one interview. MAIN FINDINGS: Participants were frequent users of health care services. Most had not been asked about ACEs by their family physicians. Most participants believed that their history of ACEs was important to their health and that providers should ask about childhood experiences. When participants discussed their primary care experiences, the following 6 common themes evolved: the importance of continuity of care; challenges with family medicine residents; provider awareness of abuse history; distress due to triggering events; characteristics of clinic staff and space; and engagement in care plans and choice. These discussions revealed that participants' primary care experiences were not always informed by the principles of trauma-informed care. CONCLUSION: Understanding the effect of ACEs on women's health is important. Incorporating a trauma-informed approach can be beneficial and enhance the experience of patients. Physicians should learn to ask patients about their childhood experiences, as it is important to their health care.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".