Geographic Information Systems for Healthcare Organizations
Bibliographic record
Abstract
The sharing of spatial information among members of the health sector can have vast strategic and operational benefits. Geographic Information Systems, or GIS, can be a key technology in optimally using this information. There are two types of applications under GIS: (1) studying health outcomes and epidemiology and (2) studying and informing healthcare delivery. With the advent of GIS that can be used over the Internet, a wider audience of decision makers and stakeholders now has the opportunity to use these technologies through something as simple as a Web browser. There is a small but growing number of published articles giving examples of using GIS for nursing practice and research. However, increased efforts are needed to make nurses, other health professionals, and health organizations aware of the possibilities of these information products for empowering their decision making. An incremental "capacity building" approach is proposed as the best way forward for sustainable and sustained nursing GIS development. The aims of this article are (1) to provide a brief nontechnical overview for readers not familiar with GIS, (2) to provide a framework for the adoption of GIS in health service organizations, and (3) to identify ways in which GIS can impact on the nursing management of patients.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.032 |
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".