Measuring geographic proximity and continuity with family medicine at end-of-life: Protocol for a population-level retrospective cohort study using Canadian Health Administrative Data
Bibliographic record
Abstract
BACKGROUND: Family physicians play an important role in coordinating care for medically complex patients, especially during the end-of-life (EOL) period. While continuity of care (COC) is a routinely measured care quality indicator, the influence of geographic proximity to family physicians on EOL COC has not been studied in the Canadian context. Existing research has focused on rurality indicators instead of individual-level proximity measures. OBJECTIVES: This study objectives are to: (1) measure the association between patients' geographic proximity to their family physician and COC during the patients' last year of life; and (2) measure the association between geographic proximity and the number of days spent in the community and palliative homecare services referral in the last year of life, and place of death. METHODS: We will conduct a population-level retrospective cohort study using linked health administrative data from ICES in Ontario, Canada, of adults who died between January 1, 2021, and December 31, 2024. Geographic proximity to the rostered family physician will be calculated in the shortest travel distance and time from the patient's residence to the physicians primary practice location, considering road, transit, and walking infrastructure. COC will be measured using three indices: Usual Provider of Care, Modified Bice-Boxerman, and Relative Variance indices, based on outpatient visits in the last year of life. EOL outcomes will include days spent in the community, referral to palliative home care, and place of death. Multivariate regression will measure associations between proximity and outcomes, adjusting for relevant patient-level characteristics. EXPECTED OUTCOMES: We hypothesize that patients living closer to their family physician will experience higher COC and improved healthcare outcomes at the end of life. Findings have the potential to inform health policy and planning aimed at improving equitable geographic access to family medicine during the late stages of life.
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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 | 0.029 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".