Factors associated with glycemic control, hospitalization, and mortality among Newfoundland and Labrador residents with diabetes mellitus
Bibliographic record
Abstract
Diabetes is among the most prevalent chronic conditions in Canada. It is characterized by a body's inability to produce or efficiently use insulin. Diabetes can be effectively managed, often with the support of a primary care provider. Unfortunately, the high turnover rate of family physicians (FPs) in Newfoundland and Labrador (NL) threatens continuity of care, potentially contributing to poorer health outcomes for people with diabetes. This study examines the relationship between FP turnover and glycemic control, hospitalization, and mortality among patients with diabetes in NL. It was hypothesized that FP turnover would be related to the three outcomes of interest. Specifically, it was expected that high turnover rates would be associated with poorer glycemic control and increased risk of hospitalization and mortality. To examine these hypotheses, a cross-sectional analysis of adults (20+ years) with diabetes in NL between 2011 and 2015 was performed. Secondary data sources were linked, including the provincial Chronic Disease Registry and the Physician and Medical Practice Database. Multivariate binary logistic regression was used to examine the relationship between FP turnover, glycemic control, hospitalization, and mortality while controlling for important covariates. The analyses provided mixed support for the hypotheses. FP turnover was found to be associated with glycemic control and hospitalization but not mortality. Further, FP turnover was associated with an increased risk of hospitalization, but the direction of the relationship between turnover and glycemic control was inconsistent with the hypothesis. Findings from this study also suggest that regions with no FPs performed similarly to regions with low turnover on the outcomes of glycemic control and hospitalization. These findings may have implications for the delivery of primary care in NL. Future studies should examine primary care models in regions with no FPs and consider whether these models could be more widely applied in NL for chronic disease management. Additionally, studies in other provinces should compare regions with high rates of primary care change (i.e., high turnover) to regions with consistent primary care availability to determine whether consistent primary care delivery, irrespective of the presence of FPs, is associated with better outcomes for patients with chronic condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".