Bibliographic record
Abstract
MEDICAL SAVINGS ACCOUNTS (MSAS) HAVE BEEN SUGGESTED as a possible solution to Canada’s health care funding woes. This approach is intended to reduce demand for health services by making individuals financially responsible for their pattern of consumption. MSAs may have appeal in the private insurance industry. A review of the scant literature on the experience in the public systems of Singapore and China, where such plans have been implemented, and on a simulation using United States Medicare data, suggests that the approach alone has not controlled costs and may increase inequalities in publicly funded systems. The conclusion is that current knowledge of MSAs is too limited to recommend their incorporation into the Canadian health care system. Alleviating acute fiscal duress has dominated discussions of health care systemreform in Canada over the last decade,1,2 and the continuing focus on sus-tainability suggests that this policy debate is still far from being resolved.3 One potential solution to the health care funding dilemma that has surfaced re-cently in Canadian academic4,5 and political circles6,7 is the establishment of Medical Savings Accounts (MSAs). This “demand-side ” approach is intended to reduce
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.222 | 0.048 |
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".