Spatio-Temporal Multi-Criteria Analysis - Conceptual Challenges and Application to Health Service Planning
Why this work is in the frame
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Bibliographic record
Abstract
Population health is influenced by many socioeconomic and demographic factors that may include levels of employment, income, education, ethnicity and age. For health planning and service delivery, it is important to take into account demographic trends over time. This temporal component is usually incorporated into analyses by comparing multiple maps of variables at different points in time. In this study demographic variables with spatial and temporal components are used in a multi-criteria analysis within an interactive spatial decision support tool. We illustrate how the exploration of an area-based composite index over time can help analysts with identifying trends of increasing social deprivation and health-care needs. The paper focuses on the conceptual challenges of spatio-temporal multi-criteria analysis due to changing geographic boundaries, the standardization of variables across time, comparability of variables, and comparability of index scores.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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