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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».