Impact of chronic disease integration strategies on healthcare outcomes and costs in Canada: A scoping review
Bibliographic record
Abstract
There has been a growing body of evidence suggesting that chronic care integration strategies can improve healthcare outcomes and decrease costs. In fact, according to the World Health Organization (2002), there is an opportunity to improve health care for chronic conditions and to curtail the growth in healthcare expenditures through integration and coordination. The purpose of this scoping review is to evaluate the impact of chronic disease integration strategies on healthcare outcomes and costs in Canada. For the purpose of this study, the words chronic disease, healthcare costs, integration and Canada were searched for in five peer-reviewed databases. The inclusion criteria included papers published from 2006 to 2020 in English. The results showed 37 papers that met the inclusion criteria, 51 per cent of which were published from 2006 to 2015 and 49 per cent from 2016 to 2020. The papers were critically appraised for quality (5 per cent strong, 11 per cent moderate, 16 per cent weak and 68 per cent not applicable); 35 per cent, however, were protocols and did not provide evidence. Only two papers demonstrated improved healthcare outcomes and decreased utilisation. Although there was insufficient evidence, this review contributes by highlighting the need to further implement chronic disease integration strategies to demonstrate healthcare outcomes and costs. This review identifies research gaps and provides directions for researchers and implications for healthcare managers and policymakers.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".