An Analysis of Primary Care Physician Accessibility and Medical Resource Distribution in Eastern Quebec: Utilizing an Enhanced Two-Step Floating Catchment Area (E2SFCA) Methodology
Bibliographic record
Abstract
Objective: The aim of this study is to evaluate the accessibility of primary care physicians (PCPs) in Eastern Quebec by employing an enhanced two-step floating catchment area (E2SFCA) methodology. This approach will facilitate the identification of patterns in primary care accessibility that may not be readily apparent through the use of regional availability measures. Methods: To evaluate the accessibility of primary care physicians (PCPs), an enhanced two-step floating catchment area methodology was utilized. This approach considers both the population supply of PCPs at the dissemination area level and the travel time between PCPs and dissemination areas. Additionally, a continuous distance decay function (β) was employed. Results: The enhanced two-step floating catchment area (E2SFCA) methodology is effective in identifying possibly underserved areas that could have otherwise appeared to have sufficient access when evaluated using traditional provider-to-population ratios. The availability of primary care services is contingent upon the presence of adequate road infrastructure. Populations residing in areas with limited access to main road networks may experience compromised access to primary care. This issue disproportionately affects vulnerable individuals who lack private transportation options. Conclusions: The application of the enhanced two-step floating catchment area methodology can facilitate the identification of areas experiencing a shortage of primary care physicians. This information can be used to inform the development of regional medical workforce programs, such as "plans régionaux d’effectifs médicaux", and to support the establishment of rural residency initiatives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".