An Overview of Chronic Disease Models: A Systematic Literature Review
Bibliographic record
Abstract
AIMS: The objective of our study was to examine various existing chronic disease models, their elements and their role in the management of Diabetes, Chronic Obstructive Pulmonary Disease (COPD), and Cardiovascular diseases (CVD). METHODS: A literature search was performed using PubMed and CINHAL during a period of January 2003- March 2011. Following key terms were used either in single or in combination such as "Chronic Disease Model" AND "Diabetes Mellitus" OR "COPD" OR "CVD". RESULTS: A total of 23 studies were included in the final analysis. Majority of the studies were US-based. Five chronic disease models included Chronic Care Model (CCM), Improving Chronic Illness Care (ICIC), and Innovative Care for Chronic Conditions (ICCC), Stanford Model (SM) and Community based Transition Model (CBTM). CCM was the most studied model. Elements studied included delivery system design and self-management support (87%), clinical information system and decision support (57%) and health system organization (52%). Elements including center care on the patient and family (13%), patient safety (4%), community policies (4%), built integrated health care (4%) and remote patient monitoring (4%) have not been well studied. Other elements including support paradigm shift, manage political environment, align sectoral policies for health, use healthcare personnel more effectively, support patients in their communities, emphasize prevention, identify patient specific concerns related to the transition process, and health literacy between visits and treatments have also not been well studied in the existing literature. CONCLUSIONS: It was unclear to what extent the results generated is applicable to different populations and locations and therefore is an area of future research. Future studies are also needed to test chronic disease models in settings where more racially and ethnically representative patients receive chronic care. Future program development should also include information on other barriers including transportation issues, finances and lack of services.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".