Trauma-informed care within and across systems of care
Bibliographic record
Abstract
Trauma has been described as a pressing public health concern and research evidence demonstrates how unresolved trauma can lead to multiple co-morbidities including chronic medical conditions such as cardiovascular disease. Furthermore, epidemiological evidence demonstrates the high prevalence of trauma histories amongst service users seeking care across a range of systems including child welfare, education, health, social services and the criminal justice system. In response, the concept of trauma-informed care (TIC) has emerged, but how TIC can be conceptually defined and utilized remains unclear in the scholarly literature. This dissertation utilizes a variety of methodological approaches to explore how and under what conditions TIC can be utilized within and across systems of care to address the prevalence of trauma-affected individuals seeking care. First, a critical interpretive synthesis of the TIC literature provides an overview of how TIC can be defined and utilized through the development of a conceptual framework situating TIC within and across systems of care. A theoretical framework outlines important contextual factors, such as system arrangements as well as the political system, that can act as either barriers or facilitators to the operationalization of TIC. Second, a document analysis examines how and under what conditions TIC is utilized in adult mental health policy documents in Ontario, Canada. Finally, a case study explores what factors led to the exclusion of TIC from Ontario’s first province-wide strategy on mental health and addictions. Collectively, these three studies add several substantive, methodological and theoretical contributions regarding a cohesive understanding of what is trauma, how TIC can be defined and operationalized and the role of TIC at various levels within and across systems of care. Mobilizing sustainable and effective TIC has been demonstrated to improve the overall health and well-being of both service users and services providers, leading to stronger systems of care and healthier communities and societies at large.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".