The Utility of Exploratory Spatial Data Analysis in the Study of Tuberculosis Incidences in an Urban Canadian Population
Bibliographic record
Abstract
Human health is a key determinant of individual quality of life and effective national economic development. Public health management and disease control are important responsibilities for governments and decision makers. In this study, a GIS-based exploratory spatial data analysis (ESDA) process is used to provide insights into the demographic factors associated with incidences of Mycobacterium tuberculosis (TB) in the Greater Vancouver Regional District (GVRD), British Columbia, Canada. The data set is for the period 1995-1999 and includes variables on vital demographics and epidemiological information, together with postal code and genetic composition of TB. The missing observations in the data set and the issue of confidentiality limited the choice of analytical methods. The main objectives of this study were to (1) elaborate the GIS-based ESDA process to explore TB incidences in the GVRD, (2) integrate factor analysis and cluster analysis into the ESDA process to discover and interpret the underlying demographic factors associated with TB, and (3) highlight the shortcomings in data collection procedures for epidemiological and health research.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".