The Effect of Standardization in Multicriteria Decision Analysis on Health Policy Outcomes
Bibliographic record
Abstract
Health planners and epidemiologists have begun to use spatial analysis and Geographic Information Systems (GIS) to explore socioeconomic inequalities that can affect population health. In particular, the use of area-based composite indices, also known as deprivation indices, has been effective at incorporating multiple indicators into an analysis. We used GIS-based Multicriteria Decision Analysis (MCDA) to create a weighted index of health service need, and explored the standardization step in MCDA within a geovisualization environment. In a neighbourhood prioritization scenario for the City of Toronto, we implemented an MCDA using two common standardization techniques and three methods for standardizing cost criteria. We compared the resulting scores and rankings of neighbourhoods, and show that standardization is an important consideration in the data analysis process. We conclude with an assessment of the appropriateness of using one technique over the other as well as the potential effect on decision-making related to health policy. Keywords: Multicriteria Decision Analysis, health policy, indicator standardization.
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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 | 0.395 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".