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Trauma signature analysis: State of the art and evolving future directions.

ABSTRACT: Trauma signature (TSIG) analysis is an evidence-based method that examines the interrelationship between population exposure to a disaster, extreme event, or complex emergency, and the inter-related physical and psychological consequences for the purpose of providing timely, actionable guidance for effective mental health and psychosocial support that is organically tailored and targeted to the defining features of the event. A series of TSIG case studies has been published since 2011 and TSIG analyses of recent disasters are in process. Disaster Health intends to expedite and feature novel TSIG research focusing on late-breaking disaster events. At the current stage of development, expert consensus is sought for refining the TSIG methodology using a Delphi process. The overarching goal is to create a fully operational system to provide timely guidance for adapting disaster behavioral health support to the salient psychological risk factors in each disaster.

SUBMITTER: Shultz JM

PROVIDER: S-EPMC5314909 | biostudies-other | 2013 Jan-Mar

REPOSITORIES: biostudies-other

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Trauma signature analysis: State of the art and evolving future directions.

Shultz James M JM Neria Yuval Y

Disaster health 20130101 1

Trauma signature (TSIG) analysis is an evidence-based method that examines the interrelationship between population exposure to a disaster, extreme event, or complex emergency, and the inter-related physical and psychological consequences for the purpose of providing timely, actionable guidance for effective mental health and psychosocial support that is organically tailored and targeted to the defining features of the event. A series of TSIG case studies has been published since 2011 and TSIG a ...[more]

PMID: 28228981

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Project description:Background:The growing need for palliative care (PC) among patients with serious illness is outstripped by the short supply of PC specialists. This mismatch calls for competency of all health care providers in primary PC, including patient-centered communication, management of pain and other symptoms, and interprofessional teamwork. Simulation-based medical education (SBME) has emerged as a promising modality to teach key skills and close the educational gap. This paper describes the current state of SBME in training of PC skills. Methods:We conducted a systematic review of the literature reporting on simulation experiences addressing PC skills for clinical learners in medicine and nursing. We collected data on learner characteristics, the method and content of the simulation, and outcome assessments. Results:In a total of 78 studies, 76% involved learners from medicine and 38% involved learners from nursing, while social work (6%) and spiritual care (3%) learners were significantly underrepresented. Only 16% of studies involved collaboration between participants at different training levels. The standardized patient encounter was the most popular simulation method, accounting for 68% of all studies. Eliciting treatment preferences (50%), delivering bad news (41%), and providing empathic communication (40%) were the most commonly addressed skills, while symptom management was only addressed in 13% of studies. The most common method of simulation evaluation was subjective participant feedback (62%). Only 4% of studies examined patient outcomes. In 22% of studies, simulation outcomes were not measured at all. Discussion:We describe the current state of SBME in PC education, highlighting advances over recent decades and identifying gaps and opportunities for future directions. We recommend designing SBME for a broader range of learners and for interprofessional skill building. We advocate for expansion of skill content, especially symptom management education. Finally, evaluation of SBME in PC training should be more rigorous with a shift to include more patient outcomes.

Dataset Information

Trauma signature analysis: State of the art and evolving future directions.

Publications

Trauma signature analysis: State of the art and evolving future directions.

Similar Datasets

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