Health Information Economy: Literature Review
Bibliographic record
Abstract
INTRODUCTION: Health Information Economy (HIE) is one of the broader, more complex, and challenging and yet important topics in the field of health science that requires the identification of its dimensions for planning and policy making. The aim of this study was to determine HIE concept dimensions. METHODS: This paper presents a systematic methodology for analyzing the trends of HIE. For this purpose, the main keywords of this area were identified and searched in the databases and from among 4775 retrieved sources, 12 sources were studied in the field of HIE. RESULTS: Information Economy (IE) in the world has passed behind four paradigms that involve the information evaluation perspective, the information technology perspective, the asymmetric information perspective and information value perspective. In this research, the fourth perspective in the HIE was analyzed. The main findings of this research were categorized in three major groups, including the flow of information process in the field of health (production. collection, processing and dissemination), and information applications in the same field (education, research, health industry, policy, legislation, and decision-making) and the underlying fields. CONCLUSION: According to the findings, HIE has already developed a theoretical and conceptual gap that due to its importance in the next decade would be one of the research approaches to health science.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".