Establishing an Evidence Synthesis Capability For Psychological Health Topics in the Military Health System
Bibliographic record
Abstract
BACKGROUND: To promote evidence-based health care, clinical providers and decision makers rely on scientific evidence to inform best practices. Evidence synthesis (ES) is a key component of this process that serves to inform health care decisions by integrating and contextualizing research findings across studies. OBJECTIVE: This paper describes the process of establishing an ES capability in the Military Health System dedicated to psychological health topics. RESEARCH DESIGNS: The goal of establishing the current ES capability was to facilitate evidence-based decision-making among clinicians, clinic managers, research funders, and policymakers, through the production and dissemination of trustworthy ES reports. We describe how we developed this capability, provide an overview of the types of evidence syntheses products we use to respond to different stakeholders, and detail the procedures established for selecting and prioritizing synthesis topics. RESULTS: We report on the productivity, acceptability, and impact of our efforts. Our reports were used by a variety of stakeholders and working groups, briefed to major committees, included in official reports and policies, and cited in clinical practice guidelines and the peer-reviewed literature. CONCLUSIONS: Our experiences thus far suggest that the current ES capability offers a needed service within our health system. Our framework may help inform other agencies interested in developing or sponsoring a similar capability.
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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 | 0.850 | 0.842 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.039 | 0.018 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.008 | 0.045 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".