Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data
Bibliographic record
Abstract
Abstract Background Social inequalities in complications associated with diabetes mellitus persist. As a primary care sensitive condition (PCSC), this association could be related to differential access to primary care. Our objectives are to establish a typology of care trajectories following a new diagnosis, and to explore social determinants of trajectories. Methods We used the TorSaDe (The Care Trajectories-Enriched Data) cohort, which links Canadian Community Health Survey respondents to health administrative data. Care trajectories were mapped over a two-year period following a new diagnosis and analysed using state sequence and clustering methods. Associations between individual and geographic characteristics with trajectory types were assessed with multinomial logistic regression. Results Three trajectories were identified: Regular Family Physician (FP) Predominant, Specialist Physician Predominant, and Few Services. With Regular FP as the reference, males had higher odds of experiencing the Few Services trajectory, higher education was associated with higher odds of both the Few Services and the Specialist trajectories, and immigrants had higher odds of the Specialist trajectory. Diagnoses in a physician’s office, as opposed to in hospital, were associated with higher odds of the Regular FP trajectory. Conclusions The Regular FP trajectory most closely aligns with the management principles of the PCSC approach. We did not find strong evidence of social status privileging access to this trajectory. However, the association with location of diagnosis suggests that efforts to ensure patients diagnosed in hospital are well linked to a regular family physician for follow up may help to reduce unnecessary specialist use and meet PCSC goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".