Enhancing Veteran Community Reintegration Research (ENCORE): Protocol for a Mixed Methods and Stakeholder Engagement Project
Bibliographic record
Abstract
BACKGROUND: Veteran community reintegration (CR) has been defined as participation in community life, including employment or other productive activities, independent living, and social relationships. Veteran CR is a Veterans Health Administration priority, as a substantial proportion of veterans report difficulties with veteran CR following discharge from military service. OBJECTIVE: Enhancing Veteran Community Reintegration Research (ENCORE) is a project funded by Veterans Health Administration's Health Service Research and Development Service. The goal of ENCORE is to maximize veteran and family reintegration by promoting innovative research and knowledge translation (KT) that informs and improves equitable Department of Veterans Affairs (VA) policies, programs, and services. Overall, 2 strategic objectives guide ENCORE activities: mobilize veteran CR research and promote innovation, relevance, and acceleration of veteran CR research and KT. METHODS: ENCORE uses a mixed methods and stakeholder-engaged approach to achieve objectives and to ensure that the KT products generated are inclusive, innovative, and meaningful to stakeholders. Project activities will occur over 5 years (2019-2024) in 5 phases: plan, engage, mobilize, promote, and evaluate. All activities will be conducted remotely owing to the ongoing COVID-19 pandemic. Methods used will include reviewing research funding and literature examining the gaps in veteran CR research, conducting expert informant interviews with VA program office representatives, and assembling and working with a Multistakeholder Partnership (MSP). MSP meetings will use external facilitation services, group facilitation techniques adapted for virtual settings, and a 6-step group facilitation process to ensure successful execution of meetings and accomplishment of goals. RESULTS: As of December 2022, data collection for ENCORE is ongoing, with the team completing interviews with 20 stakeholders from 16 VA program offices providing veteran CR-related services. ENCORE developed and assembled the MSP, reviewed the VA funding portfolio and veteran CR research literature, and conducted a scientific gap analysis. The MSP developed a veteran CR research agenda in 2021 and continues to work with the ENCORE team to prepare materials for dissemination. CONCLUSIONS: The goal of this program is to improve the impact of veteran CR research on policies and programs. Using a stakeholder-engaged process, insights from key stakeholder groups are being incorporated to set a research agenda that is more likely to result in a relevant and responsive veteran CR research program. Future products will include the development of an effective and relevant dissemination plan and the generation of innovative and relevant dissemination products designed for rapid KT. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42029.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.112 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.107 | 0.021 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".