Integrating research, clinical care, and education in academic health science centers
Bibliographic record
Abstract
Purpose One of the major issues faced by academic health science centers (AHSCs) is the need for mechanisms to foster the integration of research, clinical, and educational activities to achieve the vision of evidence-informed decision making (EIDM) and optimal client care. The paper aims to discuss this issue. Design/methodology/approach This paper synthesizes literature on organizational learning and collaboration, evidence-informed organizational decision making, and learning-based organizations to derive insights concerning the nature of effective workplace learning in AHSCs. Findings An evidence-informed model of collaborative workplace learning is proposed to aid the alignment of research, clinical, and educational functions in AHSCs. The model articulates relationships among AHSC academic functions and sub-functions, cross-functional activities, and collaborative learning processes, emphasizing the importance of cross-functional activities in enhancing collaborative learning processes and optimizing EIDM and client care. Cross-functional activities involving clinicians, researchers, and educators are hypothesized to be a primary vehicle for integration, supported by a learning-oriented workplace culture. These activities are distinct from interprofessional teams, which are clinical in nature. Four collaborative learning processes are specified that are enhanced in cross-functional activities or teamwork: co-constructing meaning, co-learning, co-producing knowledge, and co-using knowledge. Practical implications The model provides an aspirational vision and insight into the importance of cross-functional activities in enhancing workplace learning. The paper discusses the conceptual and empirical basis to the model, its contributions and limitations, and implications for AHSCs. Originality/value The model's potential utility for health care is discussed, with implications for organizational culture and the promotion of cross-functional activities.
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.
How this classification was reachedexpand
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 | 0.022 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".