Proceedings of the 14th Annual Conference on the Science of Dissemination and Implementation in Health
Bibliographic record
Abstract
Background:Annually, over 600,000 adults served in U.S. trauma centers (≥ 20%) develop posttraumatic stress disorder (PTSD) and/or depression in the first year after injury.American College of Surgeons guidelines strongly recommend screening and addressing mental health recovery in traumatic injury patients.The Trauma Resilience and Recovery Program (TRRP) is a scalable and sustainable, technology-enhanced stepped model of careone of the few in the US -that provides early intervention and direct services after traumatic injury via 4 steps: education, risk screening, and brief intervention at the bedside (Step 1); symptom self-monitoring via text messaging (Step 2); mental health screening at 30 days by chatbot or phone (Step 3); and, when appropriate, referral to mental health treatment (Step 4).This presentation describes the TRRP implementation process, program acceptability, and preliminary dissemination roadmap. Methods:We used the Exploration, Preparation, Implementation, Sustainment (EPIS; Aarons et al., 2011) model to implement TRRP in four Level I-II trauma centers.First, we collaborated with center stakeholders to assess trauma center's needs, resources and workflow to identify implementation strategies (Exploration).Second, we worked with stakeholders to outline an implementation plan, taking into account center resources, workflow, and barriers to implementation to identify program adaptations (Preparation).Third, we implemented TRRP and addressed factors associated with engagement at the patient, provider, and center level (Implementation).Finally, we implemented strategies to promote long-term sustainability (Sustainability).Findings: These programs have reached more than 10,000 patients, we identified a high prevalence of PTSD and depression after discharge, and observed high patient engagement.Several lessons were learned that shaped our implementation protocol, including: model adaptations are needed for integration into center infrastructure, and early application of billing and reimbursement practices are critical to enhancing buy-in during the initial stages of implementation and promoting long-term sustainability.Implications for D&I Research: Trauma-center based, sustainable models of mental health care are needed to ensure that all patients receive the full range of services that they need.This study explored best-practice strategies for implementing and sustaining TRRP with the goal of identifying strategies to maximize adoption and sustained use of behavioral health programs in trauma centers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".