Understanding the use of geographical information systems (GIS) in health informatics research: A review
Bibliographic record
Abstract
BACKGROUND: The purpose of this literature review is to understand geographical information systems (GIS) and how they can be applied to public health informatics, medical informatics, and epidemiology. METHOD: Relevant papers that reflected the use of geographical information systems (GIS) in health research were identified from four academic databases: Academic Search Complete, BioMed Central, PubMed Central, and Scholars Portal, as well as Google Scholar. The search strategy used was to identify articles with "geographic information systems", "GIS", "public health", "medical informatics", "epidemiology", and "health geography" as main subject headings or text words in titles and abstracts. Papers published between 1997 and 2014 were considered and a total of 39 articles were included to inform the authors on the use of GIS technologies in health informatics research. RESULTS: The main applications of GIS in health informatics and epidemiology include disease surveillance, health risk analysis, health access and planning, and community health profiling. GIS technologies can significantly improve quality and efficiency in health research as substantial connections can be made between a population's health and their geographical location. CONCLUSIONS: Gains in health informatics can be made when GIS are applied through research, however, improvements need to occur in the quantity and quality of data input for these systems to ensure better geographical health maps are used so that proper conclusions between public health and environmental factors may be made.
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".