Health Check: Analyzing Trends in West Michigan 2013
Bibliographic record
Abstract
PURPOSE: Health Check provides an ongoing trend analysis of three major issues: Knowledge Foundations, Health Care Trends, and Economic Analysis. SUBJECTS: The focus of the study is on a four-county area - Kent, Ottawa, Muskegon, and Allegan (KOMA). METHODS AND MATERIALS: Understanding Knowledge Foundations provides information on the supply of future workers in healthcare. Analysis of graduation rates and jobs data details supply and demand for the local industry; medical patents give insight to local innovation. Understanding Health Care Trends is beneficial for preventative measures and areas to focus efforts on. The metrics used to monitor these trends include demographics, risk profiles, diseases, and overall health status. Economic Analysis provides comparable results to benchmark the industry’s economic growth in the region. The comparable data pieces include other medical cities similar to Grand Rapids, a hospital survey analysis, and cost analysis of major medical conditions with emphasis on diabetes. ANALYSIS: Data were collected and weighted accordingly for the specific region of interest, in this case KOMA, from several databases and governmental resources, and from Priority Health and Blue Cross Blue Shield. RESULTS: Education facilities are graduating students with healthcare degrees at a rate that will supply the market needs for the foreseeable future; in some cases, there is a surplus of graduates for specific programs. Medical patents are remaining steady thanks in part to the Van Andel Research Institute. Health care trends in West Michigan fall in line with national trends, some instances are more promising than others. As a community, obesity and diabetes is on the rise, along with asthma. Obesity is the largest challenge our healthcare system faces in the future, and the changing demographics will compound the issue. As an industry, the healthcare system in West Michigan is growing. This is a result of either an unhealthier population, a sign the industry is drawing from a greater geographic area, or an increase in healthcare access. In terms of being a medical tourist location, Grand Rapids is gaining ground, but still trails behind Cleveland. It is also cost effective to conduct all tests and evaluations on diabetes patients during upon an initial examination. Conclusion: It is apparent the healthcare system in West Michigan is alive and well, and will continue to meet the needs of the local population. The labor force is strong; the educational structures are intact; the demand for services will continue to grow as the baby boomers continue to age; diabetes and obesity will continue to plague the community and health care systems. It is well documented that as we age past 40, our body composition changes over time by replacing muscle with fat. This physiological effect will be a major contributor to the deterioration of the quality of life of an aging population without education, community involvement, and accessible healthcare.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".