Data spatial aggregation issues in public health analysis case study for the Toronto's inner city
Bibliographic record
Abstract
This study examines the impacts of spatial aggregations of data on the outcomes of univariate and bivariate statistical analyses in an epidemiological context. Health and socioeconomic variables are used to examine a changing interdependency between these two elements at various levels of spatial aggregation. In order to complete these tasks a series of spatial data aggregations (scaling) and regroupings (zoning) are conducted and selected standard statistics are calculated at each of these levels. This study evaluates also the appropriateness of the use of standard statistical units in the settings of public health studies. An overview of selected contemporary issues in spatial statistics and epidemiology precedes the case analysis. The findings of this study reveal that spatial aggregation and rezoning of health and SES data have a substantial influence on the results of statistical analyses. Standard spatial units show less desirable characteristics for the study of the impacts of an income variable on the rates of avoidable hospitalizations than the custom units constructed in this study. These alternative spatial units are proposed as more informative than the standard ones based on comparative statistical outcomes for the area of Toronto's inner city at the neighbourhood level.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".