34 Using Spatial Analysis to Identify Access to IFHP-registered Paediatric Providers in Ontario
Bibliographic record
Abstract
Abstract Introduction/Background Compared to non-refugees, child refugees face a wide array of health disparities, including higher levels of nutritional deficiencies, infectious diseases, and mental illness secondary to experienced poverty, trauma, and migration. After migration, refugee families continue to face health inequities, necessitating access to health care. In Canada, the Interim Federal Health Program (IFHP) provides temporary health care benefits to refugees and refugee claimants who are ineligible for provincial or territorial health insurance. The program requires healthcare providers to register to be reimbursed for care provided. However, physicians are not required to register for IFHP, leading to fewer providers. Objectives This study aimed to map where IFHP-registered primary care physicians (PCPs) and paediatricians live and work in Ontario, relative to where IFHP-insured patients live, to identify geographic accessibility of primary healthcare services for paediatric refugee populations. Design/Methods We performed spatial analysis using ArcMap (version 10.8.2), a geographic information system (GIS). The practice location of PCPs and paediatricians registered for IFHP as of December 2021 were geocoded and the percent of IHFP provider per population for each Census Tract (CT) was calculated. Refugee populations eligible for IFHP in Ontario by CT, as identified by the 2016 Canadian Census, were calculated as percentages and per 10,000 population, and linked to geospatial data for comparison. Ratios of IFHP providers to refugees were calculated for each CT. Results At the time of data collection, there were 481,710 documented refugees living in Ontario, and 11,854 IFHP-registered paediatricians or PCPs. Toronto, Windsor, and Kitchener-Waterloo had the largest percentages of refugees per population (5.4%, 5.1%, and 4.6%, respectively), while Toronto, Hamilton, and London had the highest percentage of IFHP providers per population (0.13%, 0.11%, and 0.09%, respectively). Generally, CTs with the highest proportion of refugees had more IFHP-registered providers, and rural areas had the fewest number of both refugees and IFHP-registered providers. However, there were 941 CTs recognized across Ontario with identified refugee populations but no registered IFHP providers. The CTs with the highest ratio of IFHP providers to refugees were in Toronto and Ottawa (3.9, 2.0 in Toronto, and 2.1 in Ottawa, respectively). Conclusion While some regions with large refugee populations have registered IFHP PCP providers, there are regions that are relative IFHP provider-deserts for resident refugees. Identifying areas where geographic barriers are likely to exist in Ontario is necessary to improve accessibility of existing services and could help reduce healthcare inequities faced by the refugee population.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".