Relationship between Neighborhood Characteristics and Demand for Emergency Health Service Vehicles: A Poisson Hurdle Regression Modeling Approach
Bibliographic record
Abstract
The main objective of this study is to explore the spatial and temporal variability of demand for emergency health service vehicles, measured at the 1 km-by-1 km grid level in Halifax, Nova Scotia, Canada. This study utilizes and compares a Poisson regression and Poisson hurdle regression model that examine the effects of neighborhood characteristics on emergency health service vehicle demand. It also develops a time-segmented model to investigate the temporal variability of the effects of factors considered in relation to demand. It analyzes the Nova Scotia emergency health service administrative database for the period from January 2012 to December 2012. A comprehensive set of socio-demographic attributes, land use characteristics, and measures of accessibility to services are used to achieve the objectives of this research. Results found that demand for emergency health service vehicles was higher in areas where there was higher population density, more heterogeneous land uses, and a larger proportion of the population aged 40 years and above. The time-segmented model shows demand was highest during morning peak periods for residential areas, afternoon peak for commercial areas, and morning peak and midday for members of the population aged over 75 years. An assessment of the time-segmented model suggests that the generic model is sufficient for predicting how neighborhood characteristics relate to the demand for emergency health service vehicles. The findings of this study will be beneficial for urban planners and health professionals in designing healthy cities and targeting health promotions to reduce the need for emergency services.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".