Systems Thinking Tools For Identification, Assessment, intervention, and Evaluation of Traumatic Brain injury (Tbi) From intimate Partner Violence (Ipv) : Canadian indigenous Women As a Paradigmatic Case
Bibliographic record
Abstract
The Centers for Disease Control and Prevention estimate that 38 million women (1:4) in the US have experienced IPV. Between 60-92% receive associated facial or head injuries. By even the most conservative estimates, the number of women receiving TBI from IPV is greater than the number of women with breast cancer. In spite of high prevalence and strong evidence of physical, cognitive, behavioral, and psychological impacts across role domains, TBI from IPV remains under-acknowledged and understudied, resulting in knowledge and service gaps that put millions of womenu2019s lives at risk. Populations occupying marginalized social locations bear disproportionate burden in incidence, severity, and negative outcomes of TBI from IPV.In Canada, Indigenous women recipients of TBI from IPV face numerous multi-level barriers to recovery owing to their unique position at the intersection of two disciplines that rarely overlap (TBI and violence work) and being part of a socially, economically, and historically marginalized population. They are at higher risk for TBI from IPV and have access to fewer supports across levels: culture of shame and stigma around TBI and IPV; lack of accessible healthcare and shelter resources; language barriers, and; a history of colonialism and exploitation by medical, legal, and governmental structures often resulting in current experiences of judgment, paternalism, and retraumatization when engaging with systems ostensibly designed to help them. TBI from IPV in the Indigenous context is an example of a u201cwicked problemu201d: one that involves multiple, interacting human and non-human agents evolving over time in constantly changing contexts and often in non-linear, complex patterns. Systems thinking is a broad term associated with theories, methods, and tools that have been developed and deployed in diverse disciplines to address wicked problems. Systems thinking entails investigating interrelationships, behaviors, and outcomes of complex systems at high and granular levels. In this way, these paradigms allow us to connect individual outcomes to broader structures, institutions, and sociocultural dynamics. Addressing the dynamic, interacting complexities of TBI from IPV at personal, community, and institutional levels requires approaches of commensurate complexity, engaging the wicked problem on multiple levels and across sectors. Systems thinking approaches offer innovative, effective tools and models for better understanding the complexities of TBI from IPV and helping professionals locate their opportunities and responsibilities, regardless of their level or role. Examples of systems thinking approaches will be discussed across points of engagementu2014policymaking, research, health systems, community organizations, individual and family contextsu2014with concrete methods and tools such as systems dynamics and agent-based modeling, causal loop diagramming, group model building, and process mapping.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".