Geographic information systems (GIS): an emerging method to assess demand and provision for rehabilitation services
Bibliographic record
Abstract
PURPOSE: To illustrate the application of geographic information systems (GIS) as a tool to assess rehabilitation service delivery by presenting results from research recently conducted to assess demand and provision for community rehabilitation service delivery in Ontario, Canada. METHODS: Secondary analysis of data obtained from existing sources was used to establish demand and provision profiles for community rehabilitation services. These data were integrated using GIS software. RESULTS: A number of descriptive maps were produced that show the geographical distribution of service provision variables (location of individual rehabilitation health care providers and location of private and publicly funded community rehabilitation clinics) in relation to the distribution of demand variables (location of the general population; location of specific populations (i.e., residents age 65 and older) and distribution of household income). CONCLUSIONS: GIS provides a set of tools for describing and understanding the spatial organization of the health of populations and the distribution of health services that can aid the development of health policy and answer key research questions with respect to rehabilitation health services delivery. Implications for Rehabilitation It is important to seek out alternative and innovative methods to examine rehabilitation service delivery. GIS is a computer-based program that takes any data linked to a geographically referenced location and processes it through a software system that manages, analyses and displays the data in the form of a map, allowing for an alternative level of analysis. GIS provides a set of tools for describing and understanding the spatial organization of population health and health services that can aid the development of health policy and answer key research questions with respect to rehabilitation health services delivery.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.028 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".