Developing Health Neighbourhood Catchments for Health System Planning in Ontario, Canada
Bibliographic record
Abstract
Context The Frontenac Lennox and Addington Ontario Health Team (FLA OHT) is developing a health system planning tool to inform decision-making on primary care attachment and access at the Primary Care Network (PCN) table. Objective This work describes the FLA population using geo-spatial and administrative data (primary care attachment, chronic disease prevalence), sociodemographic data from the 2021 Census, social determinants of health from national surveys, and primary care contextual data to create “Health Neighbourhoods.” Study Design and Analysis Using census boundaries (Dissemination Blocks), an enhanced 2-step floating catchment method was applied to attributed boundaries within FLA, with unattached patient rates as the primary access variable. Drive time and other variables were used as decay functions to model geo-spatial changes in accessibility. The model considered primary care provider ratios and available physicians at each location. Catchments were refined using health equity data from the 2021 Census Ontario Marginalization Index, the South East Health Unit, and with help from the PCN. Setting or Dataset The study covered the FLA region of Ontario, including urban, semi-urban, and rural/remote settings. Data sources included Statistics Canada, INSPIRE-Primary Health Care, and ESRI Canada. Population Studied The entire attributed population of the FLA catchment was included, focusing on those needing attachment to primary care. Intervention/Instrument A geo-spatial approach was used to create a data-informed planning tool for regional decision-makers on primary care access and attachment. Outcomes Measured Access parameters for primary care providers were calculated at several census levels and aggregated to create local primary care catchments for further population health analysis, including chronic condition prevalence and health equity indicators. Results Geo-spatial modeling in the FLA region led to the development of primary care catchments describing the need for services. Catchments vary in size between urban and rural settings, with non-contiguous bounds to account for service delivery overlap. The access parameter highlights the potential to tune model outputs with health equity indicators. Conclusions Creating health neighbourhood catchments based on unattached primary care data using a geo-spatial supply-demand model is feasible and will support ongoing planning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".