1MSAs: Even Less Than Meets The Eye
Bibliographic record
Abstract
Medical Savings Accounts (MSAs), according to their supporters, are the quintessential cure-all. They have variously been advocated as a method to reduce government expenditure on the health care system, reduce the growth rate of health expenditures, reduce taxes, expand the range of services accessible to Canadians, help the poor, benefit the chronically ill, increase expenditure on preventive services and thereby save money in the long run while making Canadians healthier, change the nature of the physician-patient relationship, eliminate waiting lists and revitalise the health care system. This is a major set of claims for a financing arrangement. We focus on one issue: the impact that substituting MSAs for the current methods of financing hospitals and physician services in Canada is likely to have on the level of government expenditure required to provide health care to Canadians. Our results suggest that, rather than fall, these expenditures will increase substantially unless coverage is cut to the extent that Canadians are forced to pay such large amounts for their healthcare out of pocket, that insurance coverage has effectively been eliminated. No feasible method of tailoring MSAs to individual needs on the basis of age, sex, income and health status can eliminate this cost increase. We
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.199 | 0.023 |
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".