Intellectual capital in the healthcare sector: a systematic review and critique of the literature
Bibliographic record
Abstract
BACKGROUND: Variations in the performance of healthcare organizations may be partly explained by differing "stocks" of intellectual capital (IC), and differing approaches and capacities for leveraging IC. This study synthesizes what is currently known about the conceptualization, management and measurement of IC in healthcare through a review of the literature. METHODS: Peer-reviewed papers on IC in healthcare published between 1990 and 2014 were identified through searches of five databases using the following key terms: intellectual capital/assets, knowledge capital/assets/resources, and intangible assets/resources. Articles deemed relevant for inclusion underwent systematic data extraction to identify overarching themes and were assessed for their methodological quality. RESULTS: Thirty-seven papers were included in the review. The primary research method used was cross-sectional questionnaires focused on hospital managers' perceptions of IC, followed by semi-structured interviews and analysis of administrative data. Empirical studies suggest that IC is linked to subjective process and performance indicators in healthcare organizations. Although the literature on IC in healthcare is growing, it is not advanced. In this paper, we identify and examine the conceptual, theoretical and methodological limitations of the literature. CONCLUSIONS: The concept and framework of IC offer a means to study the value of intangible resources in healthcare organizations, how to manage systematically these resources together, and their mutually enhancing interactions on performance. We offer several recommendations for future research.
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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 | 0.036 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".