Geozones: an area-based method for analysis of health outcomes.
Bibliographic record
Abstract
BACKGROUND: Administrative datasets often lack information about individual characteristics such as Aboriginal identity and income. However, these datasets frequently contain individual-level geographic information (such as postal codes). This paper explains the methodology for creating Geozones, which are area-based thresholds of population characteristics derived from census data, which can be used in the analysis of social or economic differences in health and health service utilization. DATA AND METHODS: With aggregate 2006 Census information at the Dissemination Area level, population concentration and exposure for characteristics of interest are analysed using threshold tables and concentration curves. Examples are presented for the Aboriginal population and for income gradients. RESULTS: The patterns of concentration of First Nations people, Métis, and Inuit differ from those of non-Aboriginal people and between urban and rural areas. The spatial patterns of concentration and exposure by income gradients also differ. INTERPRETATION: The Geozones method is a relatively easy way of identifying areas with lower and higher concentrations of subgroups. Because it is ecological-based, Geozones has the inherent strengths and weaknesses of this approach.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".