Improving the Evaluation of Primary Care Physician Accessibility in Eastern Quebec: Incorporating the Pampalon Deprivation Index into the Enhanced Two-Step Floating Catchment Area Methodology
Bibliographic record
Abstract
This study aimed to refine the Enhanced Two-Step Floating Catchment Area (E2SFCA) methodology by incorporating the Pampalon Deprivation Index (PDI) to enable the characterization of the local environment and facilitate the identification of primary care accessibility patterns in Eastern Quebec. The E2SFCA-PDI methodology considers the supply of PCPs at the dissemination area level, travel time between PCPs and dissemination areas, and a linear distance decay function in conjunction with the PDI for each dissemination area. The E2SFCA-PDI methodology was found to be capable of identifying underserved areas that may appear to have sufficient access levels when evaluated using the standard E2SFCA method. The availability of primary care services is contingent upon the presence of adequate road infrastructure, and populations residing in areas with limited access to main road networks may experience compromised access to primary care. The study concludes that the implementation of the E2SFCA-PDI methodology can improve the identification of primary care physician shortage areas by taking into account the health needs of the population, and can aid in the development of regional medical planning and resources redistribution.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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".