Mapping where patients access primary care providers.
Bibliographic record
Abstract
ObjectivesTo gain an understanding of the attribution of patients to newly introduced Ontario Health Teams (OHT). OHTs are responsible for organizing and delivering health local care based on established connections between patients, their primary care providers, and hospitals. Furthermore, we aim to identify areas with poor geographic access to care. ApproachWe used GIS analyses and maps to depict the attribution of patients to OHTs based on their uptake of primary care and hospital referral patterns. Residents of a specific local area can be attributed to different OHTs based on their prevailing health seeking choices. This leads to a creation of non-unique OHT ‘capture zones’, which may pose challenges in primary health care planning and delivery. The range of spatial analyses and maps used in this study helps to overcome some of these limitations and provides healthcare administrators with important geographic layer of information not available through other data summary methods. ResultsThe distribution of patients and patterns of the primary care seeking vary greatly between urban, rural and remote areas. Many of the rural and remote OHTs have their patients clustered in areas surrounding the main hospital. These areas can be quite large geographically but their extents are still unique from other OHTs. OHTs in urban areas show substantial overlaps of their patient base. The urban patients are in most cases highly clustered around the main hospital location for hospitals providing primary and secondary care. The distribution of patients attributed to OHTs with hospitals providing tertiary care is quite spread out throughout the region or even the province. All these unique patterns reflect complex ways of primary care seeking behavior and referral patterns for hospital care. ConclusionThese attribution maps and data tables are an essential resource for planners and decisions makers in identifying priorities within the regional provision of primary care. This knowledge is essential to a better understanding of health care needs of local populations, and to implementing improvements in health care access.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".