Information as Change Agent or Barrier in Health Care Reform?
Bibliographic record
Abstract
Health care systems across the world are in a state of flux. If the experience of the early 1990s can be used as a model, the recent global economic downturn will lead to very significant pressures to reduce spending and achieve better value. Systems have provided a range of approaches to modeling and evaluating these more complex organizations, from simple process models to complex adaptive systems. This paper considers the pros and cons of such approaches and proposes a new modeling approach that combines the best elements of other techniques. This paper also describes a case study, where the approach has been deployed by the authors. The case study comes from health care services in Ontario, Canada, who are shifting from the traditionally hospital-based system to one that recognizes a greater role for community and primary care 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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.019 | 0.040 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".