Impact of a chronic disease self-management program on healthcare utilization in eastern Ontario, Canada
Bibliographic record
Abstract
This study aims to examine patients' patterns of health care utilization before and after participation in a Chronic Disease Self-Management Program (CDSMP). We conducted a pre-post study using health care administrative data from 186 individuals in the Ottawa region who participated in our CDSMP between September 2009 and January 2011. We collected the number of general practitioner/specialist visits, planned/unplanned emergency department visits, and hospitalizations, measured 6 months and 1 year before and after participation in the CDSMP. Multivariate analysis was performed to identify associations between patient characteristics and pre-post CDSMP health care utilization. CDSMP participation showed no effect on number of physician visits, hospitalizations, or emergency department visits. Individuals with > 5 chronic conditions were more likely to visit a physician and the emergency department following the CDSMP than those with 1 chronic condition. Among individuals > 61 years of age, those with the marital status widowed were more likely to visit their physician and the emergency department following the CDSMP than married individuals. To conclude, the CDSMP appeared not to decrease health care utilization. Low baseline utilization rates, short-term follow-ups, and a relatively healthy patient population may have contributed to the program's low impact.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".