Creating the Framework for Cross-Sector Health Analysis for Local Communities
Bibliographic record
Abstract
IntroductionA newly developed BC Ministry of Health geography classification has enabled a standardized approach for community-level analysis of health needs and service provision. An innovative methodology was developed and applied to health administrative data, creating more opportunities to identify variations in health status and utilization across the health system. Objectives and ApproachTwo design principles informed the development of the new geographies. Firstly, they reflect where people live and the communities with which they identify, and secondly, they will assist with identifying where health services are needed for local populations. The objective was to provide the Ministry and health authorities with a framework to identify and work towards providing the optimal delivery of services at the local level. A working group was established for this project and included representatives from the Ministry, each regional health authority, Provincial Health Services Authority, First Nations Health Authority, and BC Stats. ResultsThe building block for the geography classification is the Census Dissemination Block, the lowest unit of geography available in the Standard Geography Classification maintained by Statistics Canada. The geographies were assigned urban-rural designations based on an algorithm that considered the presence of a population centre, the size of the population centre, and the proportion of the population living in it, among other aspects. One of the main goals of the urban-rural designations was to provide meaningful peer groups for cross-jurisdictional studies. The project also reengineered the methods to geocode addresses to improve accuracy to use street addresses (over past method that used postal codes) so that assignment to Census Dissemination Block would be precise. The end result was 218 community geographies with urban-rural designations. Conclusion/ImplicationsThis geography standard allows health system stakeholders to better understand of geographic variation in utilization and access to health care. The ability to link and share information to profile community health between health administrative data and Census data available from Statistics Canada is better due to improved geocoding of addresses.
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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 | 0.049 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".