Accelerating learning healthcare system development through embedded research: Career trajectories, training needs, and strategies for managing and supporting embedded researchers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Health systems and organizations seeking to achieve learning healthcare system principles are increasingly relying on embedded research teams to optimize delivery of evidence-based, high-quality care that improves patient and staff experience alike. However, building organizational capacity to conduct and benefit from embedded research may be challenging in the absence of clearer guidance on career pathways and training, as well as strategies for managing and supporting this unique workforce. METHODS: In February 2018, 115 attendees from multiple agencies, institutions and professional societies participated in a conference to accelerate development of learning healthcare systems through embedded research. Workgroups engaged in structured brainstorming discussions of key domains; 21 diverse members focused on strengthening the embedded research community through more explicit development and support of multilevel career trajectories. RESULTS: Emphasis emerged on the need for training that goes beyond traditional curricula in rigorous scientific methods to include leadership, communication, and other organizational and business skills rarely offered in research training programs. These skills are required for effective engagement of multilevel stakeholders supporting evidence-based changes in routine care. Improving readiness of other stakeholders to effectively act on evidence was noted as equally crucial, as was creation of mid-career development opportunities for researchers and implementers. CONCLUSIONS: Further development and support of the embedded research workforce will require explicit attention to novel training programs and support of researchers and the stakeholders in the systems they aim to improve. IMPLICATIONS: Strategies for improving career entry and mastery of skills that foster effective multilevel stakeholder engagement hold promise for strengthening the embedded research community and their contributions to systematic improvements in health and health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it