Organizational knowledge and capabilities in healthcare: Deconstructing and integrating diverse perspectives
Bibliographic record
Abstract
Diverse concepts and bodies of work exist in the academic literature to guide research and practice on organizational knowledge and capabilities. However, these concepts have largely developed in parallel with minimal cross-fertilization, particularly in the healthcare domain. This contributes to confusion regarding conceptual boundaries and relationships, and to a lack of application of potentially useful evidence. The aim of this article is to assess three concepts associated with organizational knowledge content-intellectual capital, organizational core competencies, and dynamic capabilities-and to propose an agenda for future research. We conducted a literature review to identify and synthesize papers that apply the concepts of intellectual capital, organizational core competencies, and dynamic capabilities in healthcare settings. We explore the meaning of these concepts, summarize and critique associated healthcare research, and propose a high-level framework for conceptualizing how the concepts are related to each other. To support application of the concepts in practice, we conducted a case study of a healthcare organization. Through document review and interviews with current and former leaders, we identify and describe the organization's intellectual capital, organizational core competencies, and dynamic capabilities. The review demonstrates that efforts to identify, understand, and improve organizational knowledge have been limited in health services research. In the literature on healthcare, we identified 38 papers on intellectual capital, 4 on core competencies, and 5 on dynamic capabilities. We link these disparate fields of inquiry by conceptualizing the three concepts as distinct, but overlapping concepts influenced by broader organizational learning and knowledge management processes. To aid healthcare researchers in studying and applying a knowledge-based view of organizational performance, we propose an agenda for future research involving longitudinal comparative case studies.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".