Geographic Information Systems for Healthcare Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it