Does Higher Continuity of Family Physician Care Reduce Hospitalizations in Elderly People with Diabetes?
Bibliographic record
Abstract
The objective of this study was to investigate the relationship between continuity of family physician (FP) care and inpatient hospitalizations in elderly people with diabetes who have universally-insured health care. We constructed a population-based retrospective cohort study using a sample of 1143 people aged 65 years or older with newly diagnosed diabetes who were selected from a longitudinal surveillance database in the province of Newfoundland and Labrador (NL), Canada. Continuity of FP care was estimated by 3 chronological indices (Continuity of Care [COC], Usual Provider Continuity [UPC], and Sequential Continuity [SECON]) using administrative physician claims data. Age, sex, number of chronic conditions, and income were used as control variables. People with high continuity had lower crude rates of hospitalization than those with lower continuity. Log-linear regression analysis showed that higher continuity was associated with decreased rates of hospitalization in an unadjusted model [rate ratio (95% confidence interval)]; COC: 0.73 (0.61-0.86); UPC: 0.71 (0.59-0.86); SECON: 0.64 (0.52-0.78), and after adjusting for control variables; COC: 0.82 (0.69-0.97); UPC: 0.82 (0.68-0.98); SECON: 0.75 (0.61-0.91). Other significant predictors of reduced hospitalizations were female sex, fewer chronic conditions, and higher income. The findings suggest that high levels of continuity of FP care are associated with reduced hospitalizations in elderly people with diabetes within a universally-insured health care system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".