Evolution of studies on Real Options Theory in health sector
Bibliographic record
Abstract
Objective: The objective of this study was to identify the evolution of studies of real options theory in the health sector. For that, the present paper presents a study, which aims to analyze the studies published on the main scientific bases.Methodology/approach – A bibliometric study was developed. Articles published in: Plubmed, Wiley Online Library, Sage, Web of Science, Science Direct, Springer Link and Emerald Insight were analyzed. Data were analyzed using descriptive statisticsOriginality / Relevance: The originality and relevance is to present an analysis on the evolution of the studies of the theory of real options in the health sector already published.Main Results: The main conclusion is that the application of ROT in the health sector is not only in the evaluation of investment, but also has been observed its applicability in medical decision making. In addition, we note that the first study on real options theory in the health sector was conducted eleven years after the start of studies on real options theory. Moreover, the option to defer is the most applied in the health sector.Theoretical Contributions:This study contributes to scientific research in Applied Social Sciences by presenting an evaluation of the evolution of studies in the health sector.
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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 | 0.044 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.057 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".