Teaching Evidence Assimilation for Collaborative Health Care (TEACH) 2009-2014: Building Evidence-Based Capacity Within Health Care Provider Organizations
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines, prediction tools, and computerized decision support (CDS) are underutilized outside of research contexts, and conventional teaching of evidence-based practice (EBP) skills fails to change practitioner behavior. Overcoming these challenges requires traversing practice, policy, and implementation domains. In this article, we describe a program's conceptual design, the results of institutional participation, and the program's evolution. Next steps include integration of instruction in principles of CDS. CONCEPTUAL MODEL: Teaching Evidence Assimilation for Collaborative Health Care (TEACH) is a multidisciplinary annual conference series involving on- and off-site trainings and facilitation within health care provider organizations (HPOs). Separate conference tracks address clinical policy and guideline development, implementation science, and foundational EBP skills. The implementation track uses a model encompassing problem delineation, identifying knowing-doing gaps, synthesizing evidence to address those gaps, adapting guidelines for local use, assessing implementation barriers, measuring outcomes, and sustaining evidence use. Training in CDS principles is an anticipated component within this track. Within participating organizations, the program engages senior administration, middle management, and frontline care providers. On-site care improvement projects serve as vehicles for developing ongoing, sustainable capabilities. TEACH facilitators conduct on-site workshops to enhance project development, integration of stakeholder engagement and decision support. Both on- and off-site components emphasize narrative skills and shared decision-making. EXPERIENCE: Since 2009, 430 participants attended TEACH conferences. Delegations from five centers attended an initial series of three conferences. Improvement projects centered on stroke care, hospital readmissions, and infection control. Successful implementation efforts were characterized by strong support of senior administration, involvement of a broad multidisciplinary constituency within the organization, and on-site facilitation on the part of TEACH faculty. Involvement of nursing management at the senior faculty level led to increased presence of nursing and other disciplines at subsequent conferences. CONCLUSIONS: A multidisciplinary and multifaceted approach to on- and off-site training and facilitation may lead to enhanced use of research to improve the quality of care within HPOs. Such training may provide valuable contextual grounding for effective use of CDS within such organizations.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".