Traumatic Brain Injury Intensive Evaluation and Treatment Program: Protocol for a Partnered Evaluation Initiative Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The traumatic brain injury (TBI) Intensive Evaluation and Treatment Program (IETP) is an innovative modality for delivering evidence-based treatments in a residential, inpatient format to special operational forces service members and veterans with mild TBI. IETPs provide bundled evidence-based assessment, treatment, referral, and case management in concordance with the existing guidelines for mild TBI and commonly co-occurring comorbidities. To date, there has been no formal characterization or evaluation of the IETP to understand the determinants of implementation across the system of care. The goal of our partnered evaluation initiative (PEI) with an operational partner, the Physical Medicine and Rehabilitation National Program Office, is to facilitate the full implementation of the IETP across all 5 Veterans Health Administration TBI-Centers of Excellence (TBI-COE) and to inform minimum standards while supporting the unique characteristics of each site. OBJECTIVE: This IETP partnered evaluation will describe each of the 5 TBI-COE IETP services and state of implementation to identify opportunities for adaptation and scale, characterize the relationship between patient characteristics and clinical services received, evaluate the outcomes for participants in the IETP, and inform ongoing implementation and knowledge translation efforts to support IETP expansion. In alignment with the goals of the protocol, ineffective treatment components will be targeted for deimplementation. METHODS: A 3-year concurrent mixed methods evaluation using a participatory approach in collaboration with the operational partner and TBI-COE site leadership will be conducted. Qualitative observations, semistructured focus groups, and interviewing methods will be used to describe IETP, stakeholder experiences and needs, and suggestions for IETP implementation. Quantitative methods will include primary data collection from patients in the IETP at each site to characterize long-term outcomes and patient satisfaction with treatment and secondary data collection to quantitatively characterize patient-level and care system-level data. Finally, data sets will be triangulated to share data findings with partners to inform ongoing implementation efforts. RESULTS: Data collection began in December 2021 and is currently ongoing. The results and deliverables will inform IETP characterization, evaluation, implementation, and knowledge translation. CONCLUSIONS: The results of this evaluation seek to provide an understanding of the determinants affecting the implementation of IETPs. Service member, staff, and stakeholder insights will inform the state of implementation at each site, and quantitative measures will provide options for standardized outcome measures. This evaluation is expected to inform national Physical Medicine and Rehabilitation Office policies and processes and knowledge translation efforts to improve and expand the IETP. Future work may include cost evaluations and rigorous research, such as randomized controlled trials. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44776.
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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 | 0.121 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.080 | 0.015 |
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".